Challenges and Applications of Large Language Models

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

128 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 126 not yet.

• Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2307.10169 · arXiv 2307.10169

@misc{kaddour2023challenges,
  title = {{Challenges and Applications of Large Language Models}},
  author = {Kaddour, Jean and Harris, Joshua and Mozes, Maximilian and Bradley, Herbie and Raileanu, Roberta and McHardy, Robert},
  year = {2023},
  doi = {10.48550/arxiv.2307.10169},
  eprint = {2307.10169},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.10169}
}

References

669 references, 156 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. [1] A blog post detailed a Sam Altman freakout about a huge chips shortage threatening OpenAI. Then it was taken down. [2] Open LLM Leaderboard - a Hugging Face Space by Hug-gingFaceH4. [3] Reproducibility — PyTorch 2.0 documentation. [4] 2023. Negative prompts for text generation. Section: Promptin… find
2. [5] 2023. Reproducibility. Page Version ID: 1163331755. find
3. [18] J. Andreas. 2022. Language models as agent models. find
4. [19] C. Anil, Y. Wu, A. Andreassen, A. Lewkowycz, V. Misra, V. Ramasesh, A. Slone, G. Gur-Ari et al. 2022. Exploring Length Generalization in Large Language Models. ArXiv:2207.04901 [cs]. find our card
5. [20] R. Anil, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403. find our card
6. [21] F. Antaki, S. Touma, D. Milad, J. El-Khoury and R. Duval. 2023. Evaluating the performance of chatgpt in ophthalmology: An analysis of its successes and shortcomings. medRxiv. find
7. [6] A. Abbas, K. Tirumala, D. Simig, S. Ganguli and A. S. Morcos. 2023. Semdedup: Data-efficient learning at web-scale through semantic deduplication. arXiv preprint arXiv:2303.09540. find
8. [22] L. P. Argyle, E. C. Busby, N. Fulda, J. Gubler, C. Rytting and D. Wingate. 2022. Out of one, many: Using language models to simulate human samples. arXiv preprint arXiv:2209.06899. find our card
9. [7] J. D. Abernethy, A. Agarwal, T. V. Marinov and M. K. Warmuth. 2023. A mechanism for sample-efficient in-context learning for sparse retrieval tasks. ArXiv, abs/2305.17040. — [arXiv:2305.17040](https://arxiv.org/abs/2305.17040) find
10. [23] V. Aribandi, Y. Tay, T. Schuster, J. Rao, H. S. Zheng, S. V. Mehta, H. Zhuang, V. Q. Tran et al. 2022. Ext5: Towards extreme multi-task scaling for transfer learning. In International Conference on Learning Representations. find
11. [8] D. Adiwardana, M.-T. Luong, D. R. So, J. Hall, N. Fiedel, R. Thoppilan, Z. Yang, A. Kulshreshtha et al. 2020. Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977. find our card
12. [9] R. Agarwal, M. Schwarzer, P. S. Castro, A. C. Courville and M. Bellemare. 2021. Deep Reinforcement Learning at the Edge of the Statistical Precipice. In Advances in Neural Information Processing Systems, volume 34, pages 29304–29320. Curran Associates, Inc. find
13. [10] M. Agrawal, S. Hegselmann, H. Lang, Y. Kim and D. Sontag. 2022. Large language models are zero-shot clinical information extractors. arXiv preprint arXiv:2205.12689. find
14. [11] P. Agrawal, C. Alberti, F. Huot, J. Maynez, J. Ma, S. Ruder, K. Ganchev, D. Das et al. 2022. Qameleon: Multilingual qa with only 5 examples. arXiv preprint arXiv:2211.08264. find
15. [12] G. Aher, R. I. Arriaga and A. T. Kalai. 2022. Using large language models to simulate multiple humans. arXiv preprint arXiv:2208.10264. — [arXiv:2208.10264](https://arxiv.org/abs/2208.10264) find
16. [13] O. Ahia, S. Kumar, H. Gonen, J. Kasai, D. R. Mortensen, N. A. Smith and Y. Tsvetkov. 2023. Do all languages cost the same? tokenization in the era of commercial language models. arXiv preprint arXiv:2305.13707. find our card
17. [14] M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan et al. 2022. Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691. find our card
18. [15] J. Ainslie, T. Lei, M. de Jong, S. Ontañón, S. Brahma, Y. Zemlyanskiy, D. Uthus, M. Guo et al. 2023. Colt5: Faster long-range transformers with conditional computation. arXiv preprint arXiv:2303.09752. find
19. [24] S. Arora, A. Narayan, M. F. Chen, L. Orr, N. Guha, K. Bhatia, I. Chami, F. Sala et al. 2022. Ask me anything: A simple strategy for prompting language models. find
20. [25] A. Asai, T. Schick, P. Lewis, X. Chen, G. Izacard, S. Riedel, H. Hajishirzi and W.-t. Yih. 2022. Task-aware retrieval with instructions. find
21. [26] N. Asher, S. Bhar, A. Chaturvedi, J. Hunter and S. Paul. 2023. Limits for Learning with Language Models. ArXiv:2306.12213 [cs]. find
22. [27] M. C. Ashton and K. Lee. 2009. The hexaco–60: A short measure of the major dimensions of personality. Journal of personality assessment, 91(4):340–345. find
23. [28] J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai et al. 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732. find our card
24. [29] AUTOMATIC1111. 2023. Stable Diffusion web UI. Original-date: 2022-08-22T14:05:26Z. find
25. [30] J. W. Ayers, A. Poliak, M. Dredze, E. C. Leas, Z. Zhu, J. B. Kelley, D. J. Faix, A. M. Goodman et al. 2023. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA internal medicine. find
26. [31] Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie et al. 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073. find our card
27. [32] P. Bajaj, D. Campos, N. Craswell, L. Deng, J. Gao, X. Liu, R. Majumder, A. McNamara et al. 2018. Ms marco: A human generated machine reading comprehension dataset. find our card
28. [33] P. Bajaj, C. Xiong, G. Ke, X. Liu, D. He, S. Tiwary, T.-Y. Liu, P. Bennett et al. 2022. Metro: Efficient denoising pre-training of large scale autoencoding language models with model generated signals. arXiv preprint arXiv:2204.06644. — [arXiv:2204.06644](https://arxiv.org/abs/2204.06644) find
29. [34] A. Bakhtin, S. Gross, M. Ott, Y. Deng, M. Ranzato and A. Szlam. 2019. Real or Fake? Learning to Discriminate Machine from Human Generated Text. ArXiv:1906.03351 [cs, stat]. — [arXiv:1906.03351](https://arxiv.org/abs/1906.03351) find
30. [35] R. Balestriero, J. Pesenti and Y. LeCun. 2021. Learning in high dimension always amounts to extrapolation. arXiv preprint arXiv:2110.09485. — [arXiv:2110.09485](https://arxiv.org/abs/2110.09485) find
31. [36] J. Bandy and N. Vincent. 2021. Addressing "documentation debt" in machine learning research: A retrospective datasheet for bookcorpus. find
32. [37] P. Barham, A. Chowdhery, J. Dean, S. Ghemawat, S. Hand, D. Hurt, M. Isard, H. Lim et al. 2022. Pathways: Asynchronous distributed dataflow for ml. Proceedings of Machine Learning and Systems, 4:430–449. find
33. [38] M. Bavarian, H. Jun, N. Tezak, J. Schulman, C. McLeavey, J. Tworek and M. Chen. 2022. Efficient training of language models to fill in the middle. arXiv preprint arXiv:2207.14255. find our card
34. [39] N. Belrose, Z. Furman, L. Smith, D. Halawi, I. Ostrovsky, L. McKinney, S. Biderman and J. Steinhardt. 2023. Eliciting latent predictions from transformers with the tuned lens. find our card
35. [47] S. Black, S. Biderman, E. Hallahan, Q. Anthony, L. Gao, L. Golding, H. He, C. Leahy et al. 2022. Gpt-neox-20b: An open-source autoregressive language model. find our card
36. [48] A. Blair-Stanek, N. Holzenberger and B. Van Durme. 2023. Can gpt-3 perform statutory reasoning? arXiv preprint arXiv:2302.06100. find our card
37. [49] J. Bommarito, M. Bommarito, D. M. Katz and J. Katz. 2023. Gpt as knowledge worker: A zero-shot evaluation of (ai) cpa capabilities. arXiv preprint arXiv:2301.04408. — [arXiv:2301.04408](https://arxiv.org/abs/2301.04408) find
38. [50] M. Bommarito II and D. M. Katz. 2022. Gpt takes the bar exam. arXiv preprint arXiv:2212.14402. — [arXiv:2212.14402](https://arxiv.org/abs/2212.14402) find
39. [51] L. Bonifacio, H. Abonizio, M. Fadaee and R. Nogueira. 2022. Inpars: Unsupervised dataset generation for information retrieval. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’22, page 2387–2392, New York, NY, USA. Associ… find
40. [52] S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. v. d. Driessche, J.-B. Lespiau et al. 2021. Improving language models by retrieving from trillions of tokens. arXiv preprint arXiv:2112.04426. find
41. [53] A. Borji. 2023. A Categorical Archive of ChatGPT Failures. ArXiv:2302.03494 [cs]. find
42. [54] A. Borzunov, D. Baranchuk, T. Dettmers, M. Ryabinin, Y. Belkada, A. Chumachenko, P. Samygin and C. Raffel. 2022. Petals: Collaborative inference and fine-tuning of large models. arXiv preprint arXiv:2209.01188. find
43. [40] E. Ben Zaken, Y. Goldberg and S. Ravfogel. 2022. Bit-Fit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1–9, Dublin, Ireland. Associa… find
44. [55] L. Bos, C. Schemer, N. Corbu, M. Hameleers, I. An-dreadis, A. Schulz, D. Schmuck, C. Reinemann et al. 2020. The effects of populism as a social identity frame on persuasion and mobilisation: Evidence from a 15-country experiment. European Journal of Political Research, 59(1):3–24. find
45. [41] S. Biderman, K. Bicheno and L. Gao. 2022. Datasheet for the pile. arXiv preprint arXiv:2201.07311. — [arXiv:2201.07311](https://arxiv.org/abs/2201.07311) find
46. [56] D. Britz, M. Y. Guan and M.-T. Luong. 2017. Efficient attention using a fixed-size memory representation. arXiv preprint arXiv:1707.00110. find
47. [42] S. Biderman, U. S. Prashanth, L. Sutawika, H. Schoelkopf, Q. Anthony, S. Purohit and E. Raff. 2023. Emergent and Predictable Memorization in Large Language Models. ArXiv:2304.11158 [cs]. find
48. [43] S. Biderman and W. J. Scheirer. 2021. Pitfalls in machine learning research: Reexamining the development cycle. find
49. [44] S. Biderman, H. Schoelkopf, Q. G. Anthony, H. Bradley, K. O’Brien, E. Hallahan, M. A. Khan, S. Purohit et al. 2023. Pythia: A suite for analyzing large language models across training and scaling. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings… find
50. [45] S. R. Biderman. 2023. [...] we aren’t running out of text data any time soon. ml researchers massively underestimate how much text is out there. https://twitter.com/BlancheMinerva/ status/1644154144431677442?s=20. Accessed: 2023-05-28. ↗
51. [46] A. Birhane, V. U. Prabhu and E. Kahembwe. 2021. Multimodal datasets: misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963. — [arXiv:2110.01963](https://arxiv.org/abs/2110.01963) find
52. [57] A. Z. Broder, M. Charikar, A. M. Frieze and M. Mitzenmacher. 1998. Min-wise independent permutations. In Proceedings of the thirtieth annual ACM symposium on Theory of computing, pages 327–336. find
53. [58] G. Brown, M. Bun, V. Feldman, A. Smith and K. Talwar. 2021. When is memorization of irrelevant training data necessary for high-accuracy learning? In Proceedings of the 53rd annual ACM SIGACT symposium on theory of computing, pages 123–132. find
54. [59] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam et al. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curran Associates, Inc. find
55. [60] M. Brundage, S. Avin, J. Clark, H. Toner, P. Eckersley, B. Garfinkel, A. Dafoe, P. Scharre et al. 2018. The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228. — [arXiv:1802.07228](https://arxiv.org/abs/1802.07228) find
56. [61] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. find our card
57. [62] C. Burns, H. Ye, D. Klein and J. Steinhardt. 2022. Discovering latent knowledge in language models without supervision. find
58. [63] A. Calderwood, N. Wardrip-Fruin and M. Mateas. 2022. Spinning coherent interactive fiction through foundation model prompts. International Conference of Computation and Creativity. find
59. [64] N. Carlini, M. Jagielski, C. A. Choquette-Choo, D. Paleka, W. Pearce, H. Anderson, A. Terzis, K. Thomas et al. 2023. Poisoning Web-Scale Training Datasets is Practical. ArXiv:2302.10149 [cs]. — [arXiv:2302.10149](https://arxiv.org/abs/2302.10149) find
60. [65] N. Carlini, C. Liu, Ú. Erlingsson, J. Kos and D. Song. 2019. The secret sharer: Evaluating and testing unintended memorization in neural networks. In USENIX Security Symposium, volume 267. find our card
61. [78] M. Chen, A. Papangelis, C. Tao, S. Kim, A. Rosenbaum, Y. Liu, Z. Yu and D. Hakkani-Tur. 2023. Places: Prompting language models for social conversation synthesis. arXiv preprint arXiv:2302.03269. find
62. [79] S. Chen, S. Wong, L. Chen and Y. Tian. 2023. Extending context window of large language models via positional interpolation. find
63. [80] T. Chen, Z. Zhang, A. Jaiswal, S. Liu and Z. Wang. 2023. Sparse moe as the new dropout: Scaling dense and self-slimmable transformers. find
64. [81] X. Chen, M. Lin, N. Schärli and D. Zhou. 2023. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128. find
65. [82] L. Cheng, X. Li and L. Bing. 2023. Is gpt-4 a good data analyst? find
66. [66] N. Carlini, M. Nasr, C. A. Choquette-Choo, M. Jagielski, I. Gao, A. Awadalla, P. W. Koh, D. Ippolito et al. 2023. Are aligned neural networks adversarially aligned? find
67. [83] D. Choe, R. Al-Rfou, M. Guo, H. Lee and N. Constant. 2019. Bridging the Gap for Tokenizer-Free Language Models. ArXiv:1908.10322 [cs]. — [arXiv:1908.10322](https://arxiv.org/abs/1908.10322) find
68. [67] N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown et al. 2020. Extracting training data from large language models. find our card
69. [84] J. H. Choi, K. E. Hickman, A. Monahan and D. Schwarcz. 2023. Chatgpt goes to law school. Available at SSRN. find
70. [68] S. Casper, J. Lin, J. Kwon, G. Culp and D. Hadfield-Menell. 2023. Explore, establish, exploit: Red teaming language models from scratch. arXiv preprint arXiv:2306.09442. find
71. [69] T. Chakrabarty, V. Padmakumar and H. He. 2022. Help me write a poem: Instruction tuning as a vehicle for collaborative poetry writing. arXiv preprint arXiv:2210.13669. — [arXiv:2210.13669](https://arxiv.org/abs/2210.13669) find
72. [70] I. Chalkidis, I. Androutsopoulos and N. Aletras. 2019. Neural legal judgment prediction in english. arXiv preprint arXiv:1906.02059. find our card
73. [71] I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. 2020. Legal-bert: The muppets straight out of law school. arXiv preprint arXiv:2010.02559. find our card
74. [72] Y. Chang, X. Wang, J. Wang, Y. Wu, K. Zhu, H. Chen, L. Yang, X. Yi et al. 2023. A Survey on Evaluation of Large Language Models. ArXiv:2307.03109 [cs]. find our card
75. [73] B. Chen, X. Cheng, L. ao Gengyang, S. Li, X. Zeng, B. Wang, G. Jing, C. Liu et al. 2023. xtrimopglm: Unified 100b-scale pre-trained transformer for deciphering the language of protein. bioRxiv. find
76. [74] C. Chen, S. Borgeaud, G. Irving, J.-B. Lespiau, L. Sifre and J. Jumper. 2023. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318. find
77. [75] L. Chen, M. Zaharia and J. Zou. 2023. FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance. ArXiv:2305.05176 [cs]. find our card
78. [76] L. Chen, M. Zaharia and J. Zou. 2023. How is ChatGPT’s behavior changing over time? ArXiv:2307.09009 [cs]. — [arXiv:2307.09009](https://arxiv.org/abs/2307.09009) find
79. [77] M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda et al. 2021. Evaluating large language models trained on code. find our card
80. [85] K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis et al. 2020. Rethinking attention with performers. arXiv preprint arXiv:2009.14794. find
81. [86] A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311. find
82. [87] M. Christ, S. Gunn and O. Zamir. 2023. Undetectable Watermarks for Language Models. find
83. [88] P. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg and D. Amodei. 2017. Deep reinforcement learning from human preferences. find
84. [89] K. Christianson, A. Hollingworth, J. F. Halliwell and F. Ferreira. 2001. Thematic roles assigned along the garden path linger. Cognitive psychology, 42(4):368–407. find
85. [90] H. W. Chung. 2023. Missing model details (tweet). find
86. [91] H. W. Chung, X. Garcia, A. Roberts, Y. Tay, O. Firat, S. Narang and N. Constant. 2023. Unimax: Fairer and more effective language sampling for large-scale multilingual pretraining. In The Eleventh International Conference on Learning Representations. find
87. [92] H. W. Chung, D. Garrette, K. C. Tan and J. Riesa. 2020. Improving multilingual models with language-clustered vocabularies. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4536–4546, Online. Association for Computational Linguistics. find
88. [93] H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang et al. 2022. Scaling instruction-finetuned language models. find
89. [94] J. H. Clark, D. Garrette, I. Turc and J. Wieting. 2022. Canine: Pre-training an efficient tokenization-free encoder for language representation. Transactions of the Association for Computational Linguistics, 10:73–91. find
90. [95] K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek et al. 2021. Training verifiers to solve math word problems. find our card
91. [96] D. Cohen, M. Ryu, Y. Chow, O. Keller, I. Greenberg, A. Hassidim, M. Fink, Y. Matias et al. 2022. Dynamic planning in open-ended dialogue using reinforcement learning. arXiv preprint arXiv:2208.02294. — [arXiv:2208.02294](https://arxiv.org/abs/2208.02294) find
92. [97] R. Cohen, M. Hamri, M. Geva and A. Globerson. 2023. LM vs LM: Detecting Factual Errors via Cross Examination. ArXiv:2305.13281 [cs]. find
93. [98] T. Computer. 2023. Redpajama: An open source recipe to reproduce llama training dataset. find
94. [99] A. Conmy, A. N. Mavor-Parker, A. Lynch, S. Heimersheim and A. Garriga-Alonso. 2023. Towards automated circuit discovery for mechanistic interpretability. arXiv preprint arXiv:2304.14997. find
95. [100] A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott et al. 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440–8451, Online. Association f… find
96. [101] A. B. Cyphert. 2021. A human being wrote this law review article: Gpt-3 and the practice of law. UC Davis L. Rev., 55:401. find
97. [102] D. Dai, L. Dong, Y. Hao, Z. Sui, B. Chang and F. Wei. 2022. Knowledge neurons in pretrained transformers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8493–8502, Dublin, Ireland. Association for Computational Linguist… find
98. [103] D. Dai, Y. Sun, L. Dong, Y. Hao, Z. Sui and F. Wei. 2022. Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers. arXiv preprint arXiv:2212.10559. find
99. [104] H. Dai, Z. Liu, W. Liao, X. Huang, Z. Wu, L. Zhao, W. Liu, N. Liu et al. 2023. Chataug: Leveraging chatgpt for text data augmentation. find
100. [105] Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le and R. Salakhutdinov. 2019. Transformer-XL: Attentive language models beyond a fixed-length context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2978–2988, Florence, Italy. Association for Compu… find
101. [106] H. Dalla-Torre, L. Gonzalez, J. Mendoza Revilla, N. Lopez Carranza, A. Henryk Grywaczewski, F. Oteri, C. Dallago, E. Trop et al. 2023. The nucleotide transformer: Building and evaluating robust foundation models for human genomics. bioRxiv, pages 2023–01. find
102. [107] T. Dao, D. Y. Fu, S. Ermon, A. Rudra and C. Ré. 2022. Flashattention: Fast and memory-efficient exact attention with io-awareness. arXiv preprint arXiv:2205.14135. find
103. [108] T. Dao, D. Y. Fu, K. K. Saab, A. W. Thomas, A. Rudra and C. Ré. 2023. Hungry Hungry Hippos: Towards Language Modeling with State Space Models. ArXiv:2212.14052 [cs]. find
104. [109] S. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski and R. Liu. 2020. Plug and play language models: A simple approach to controlled text generation. find our card
105. [110] J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet et al. 2022. Robust deep learning&\#x2013;based protein sequence design using proteinmpnn. Science, 378(6615):49–56. find
106. [111] N. De Cao, W. Aziz and I. Titov. 2021. Editing factual knowledge in language models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6491–6506, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. find
107. [112] M. Dehghani, A. Arnab, L. Beyer, A. Vaswani and Y. Tay. 2022. The Efficiency Misnomer. ArXiv:2110.12894 [cs, stat]. — [arXiv:2110.12894](https://arxiv.org/abs/2110.12894) find
108. [113] M. Dehghani, Y. Tay, A. A. Gritsenko, Z. Zhao, N. Houlsby, F. Diaz, D. Metzler and O. Vinyals. 2021. The benchmark lottery. arXiv preprint arXiv:2107.07002. find
109. [114] L. Del Corro, A. Del Giorno, S. Agarwal, B. Yu, A. Awadallah and S. Mukherjee. 2023. SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference. ArXiv:2307.02628 [cs]. — [arXiv:2307.02628](https://arxiv.org/abs/2307.02628) find
110. [115] A. Deroy, K. Ghosh and S. Ghosh. 2023. How ready are pre-trained abstractive models and llms for legal case judgement summarization? find
111. [116] A. Deshpande, V. Murahari, T. Rajpurohit, A. Kalyan and K. Narasimhan. 2023. Toxicity in chatgpt: Analyzing persona-assigned language models. arXiv preprint arXiv:2304.05335. find our card
112. [117] T. Dettmers, M. Lewis, Y. Belkada and L. Zettlemoyer. 2022. Llm.int8(): 8-bit matrix multiplication for transformers at scale. find
113. [118] T. Dettmers, A. Pagnoni, A. Holtzman and L. Zettlemoyer. 2023. QLoRA: Efficient Finetuning of Quantized LLMs. ArXiv:2305.14314 [cs]. find
114. [119] T. Dettmers, R. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar, S. Ashkboos, A. Borzunov, T. Hoefler et al. 2023. Spqr: A sparse-quantized representation for near-lossless llm weight compression. arXiv preprint arXiv:2306.03078. find
115. [120] J. Devlin, M.-W. Chang, K. Lee and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (… find
116. [121] N. Dey, G. Gosal, Zhiming, Chen, H. Khachane, W. Marshall, R. Pathria, M. Tom et al. 2023. Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster. find
117. [122] S. Diao, X. Li, Y. Lin, Z. Huang and T. Zhang. 2022. Black-box prompt learning for pre-trained language models. arXiv preprint arXiv:2201.08531. — [arXiv:2201.08531](https://arxiv.org/abs/2201.08531) find
118. [123] B. Ding, C. Qin, L. Liu, L. Bing, S. Joty and B. Li. 2022. Is gpt-3 a good data annotator? arXiv preprint arXiv:2212.10450. find our card
119. [124] J. Ding, S. Ma, L. Dong, X. Zhang, S. Huang, W. Wang and F. Wei. 2023. Longnet: Scaling transformers to 1,000,000,000 tokens. find
120. [125] J. Dodge, M. Sap, A. Marasović, W. Agnew, G. Ilharco, D. Groeneveld, M. Mitchell and M. Gardner. 2021. Documenting large webtext corpora: A case study on the colossal clean crawled corpus. arXiv preprint arXiv:2104.08758. find
121. [126] R. Dominguez-Olmedo, M. Hardt and C. Mendler-Dünner. 2023. Questioning the survey responses of large language models. arXiv preprint arXiv:2306.07951. — [arXiv:2306.07951](https://arxiv.org/abs/2306.07951) find
122. [127] Q. Dong, D. Dai, Y. Song, J. Xu, Z. Sui and L. Li. 2022. Calibrating factual knowledge in pretrained language models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5937–5947, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. find
123. [128] D. R. Dowty, R. Wall and S. Peters. 2012. Introduction to Montague semantics, volume 11. Springer Science & Business Media. find
124. [129] D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson et al. 2023. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378. find
125. [130] N. Du, Y. Huang, A. M. Dai, S. Tong, D. Lepikhin, Y. Xu, M. Krikun, Y. Zhou et al. 2022. Glam: Efficient scaling of language models with mixture-of-experts. In International Conference on Machine Learning, pages 5547–5569. PMLR. find our card
126. [131] Y. Du, S. Li, A. Torralba, J. B. Tenenbaum and I. Mordatch. 2023. Improving Factuality and Reasoning in Language Models through Multiagent Debate. ArXiv:2305.14325 [cs]. find our card
127. [132] Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang and J. Tang. 2022. GLM: General language model pretraining with autoregressive blank infilling. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 320–335, Dublin, Ireland. A… find our card
128. [133] A. Dunn, J. Dagdelen, N. Walker, S. Lee, A. S. Rosen, G. Ceder, K. Persson and A. Jain. 2022. Structured information extraction from complex scientific text with fine-tuned large language models. arXiv preprint arXiv:2212.05238. — [arXiv:2212.05238](https://arxiv.org/abs/2212.05238) find
129. [134] D. Duong and B. D. Solomon. 2023. Analysis of large-language model versus human performance for genetics questions. European Journal of Human Genetics, pages 1–3. find
130. [135] N. Dziri, X. Lu, M. Sclar, X. L. Li, L. Jiang, B. Y. Lin, P. West, C. Bhagavatula et al. 2023. Faith and Fate: Limits of Transformers on Compositionality. ArXiv:2305.18654 [cs]. find our card
131. [136] N. Dziri, A. Madotto, O. Zaiane and A. J. Bose. 2021. Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding. ArXiv:2104.08455 [cs]. find
132. [137] E.-M. El-Mhamdi, S. Farhadkhani, R. Guerraoui, N. Gupta, L.-N. Hoang, R. Pinot, S. Rouault and J. Stephan. 2023. On the Impossible Safety of Large AI Models. ArXiv:2209.15259 [cs]. — [arXiv:2209.15259](https://arxiv.org/abs/2209.15259) find
133. [138] N. Elhage, N. Nanda, C. Olsson, T. Henighan, N. Joseph, B. Mann, A. Askell, Y. Bai et al. 2021. A mathematical framework for transformer circuits. Transformer Circuits Thread. find
134. [139] A. Elnaggar, M. Heinzinger, C. Dallago, G. Rihawi, Y. Wang, L. Jones, T. Gibbs, T. Feher et al. 2020. Prottrans: towards cracking the language of life’s code through self-supervised deep learning and high performance computing. arXiv preprint arXiv:2007.06225. find
135. [140] T. Eloundou, S. Manning, P. Mishkin and D. Rock. 2023. Gpts are gpts: An early look at the labor market impact potential of large language models. find
136. [141] F. Faal, K. Schmitt and J. Y. Yu. 2023. Reward modeling for mitigating toxicity in transformer-based language models. Applied Intelligence, 53(7):8421–8435. find
137. [142] A. Fan, C. Gardent, C. Braud and A. Bordes. 2021. Augmenting transformers with KNN-based composite memory for dialog. Transactions of the Association for Computational Linguistics, 9:82–99. find
138. [143] A. Fan, E. Grave and A. Joulin. 2020. Reducing transformer depth on demand with structured dropout. In International Conference on Learning Representations. find our card
139. [144] M. Fathi, J. Pilault, P.-L. Bacon, C. Pal, O. Firat and R. Goroshin. 2023. Block-State Transformer. ArXiv:2306.09539 [cs]. find
140. [145] W. Fedus, B. Zoph and N. Shazeer. 2021. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. find
141. [146] V. Feldman. 2020. Does learning require memorization? a short tale about a long tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, pages 954–959. find
142. [147] S. Feng, C. Y. Park, Y. Liu and Y. Tsvetkov. 2023. From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models. ArXiv:2305.08283 [cs]. find our card
143. [148] W. Feng, W. Zhu, T.-j. Fu, V. Jampani, A. Akula, X. He, S. Basu, X. E. Wang et al. 2023. LayoutGPT: Compositional Visual Planning and Generation with Large Language Models. ArXiv:2305.15393 [cs]. — [arXiv:2305.15393](https://arxiv.org/abs/2305.15393) find
144. [149] E. Ferrara. 2023. Should chatgpt be biased? challenges and risks of bias in large language models. arXiv preprint arXiv:2304.03738. — [arXiv:2304.03738](https://arxiv.org/abs/2304.03738) find
145. [150] A. Ficek, F. Liu and N. Collier. 2022. How to tackle an emerging topic? combining strong and weak labels for covid news NER. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural… find
146. [151] C. Fourrier, N. Habib, J. Launay and T. Wolf. [167] S. Gehman, S. Gururangan, M. Sap, Y. Choi and N. A. 2023. What’s going on with the open llm leader-Smith. 2020. Realtoxicityprompts: Evaluating neural board? Available from: https://huggingface. toxic degeneration in language models. arXiv pr… find our card
147. Brendel, M. Bethge and F. A. Wichmann. 2020. find
148. Short-tarh. 2022. Gptq: Accurate post-training quantization cut learning in deep neural networks. Nature Machine for generative pre-trained transformers. arXiv preprint Intelligence, 2(11):665–673. arXiv:2210.17323. [170] A. Glaese, N. McAleese, M. Tr˛ebacz, J. Aslanides, [154] D. Fried, A. Aghajany… find
149. F. Shi, R. Zhong, W.-t. Yih et al. 2022. Incoder: A genera-Improving alignment of dialogue agents via targeted human tive model for code infilling and synthesis. judgements. find
150. Resolv- [171] D. Goldberg. 1991. What every computer scientist [155] A. Frömmgen and L. Kharatyan. 2023. ing code review comments with ml. Available from: should know about floating-point arithmetic. ACM Com-https://ai.googleblog.com/2023/05/ puting Surveys, 23(1):5–48. resolving-code-review-comment… find
151. Ng, Z. Jiang and P. Liu. 2023. Gptscore: Improving depthwise model-parallelism. The Journal of Evaluate as you desire. arXiv preprint arXiv:2302.04166. Machine Learning Research, 23(1):7714–7741. find
152. [157] T. Fujii, K. Shibata, A. Yamaguchi, T. Morishita and Y. Sogawa. 2023. How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in japanese. arXiv preprint arXiv:2306.09572. find
153. [158] I. Gabriel. 2020. Artificial intelligence, values, and alignment. Minds and machines, 30(3):411–437. find
154. [159] S. Gadgil, A. R. Tadipatri, A. Agrawal, A. Narayanan and N. Goyal. 2022. Towards automating formalisation of theorem statements using large language models. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Workshop on MATH-AI. find
155. [160] T. Gale, D. Narayanan, C. Young and M. Zaharia. 2022. Megablocks: Efficient sparse training with mixture-of-experts. arXiv preprint arXiv:2211.15841. find
156. [161] T. Gale, M. Zaharia, C. Young and E. Elsen. 2020. Sparse GPU Kernels for Deep Learning. ArXiv:2006.10901 [cs, stat]. find
157. [162] K. Gandhi, J.-P. Fränken, T. Gerstenbrg and N. D. Goodman. 2023. Understanding social reasoning in language models with language models. arXiv preprint arXiv:2306.15448. find
158. [163] D. Ganguli, L. Lovitt, J. Kernion, A. Askell, Y. Bai, S. Kadavath, B. Mann, E. Perez et al. 2022. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858. find our card
159. [164] J. Gao, X. Ding, B. Qin and T. Liu. 2023. Is chatgpt a good causal reasoner? a comprehensive evaluation. arXiv preprint arXiv:2305.07375. find
160. [165] L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He et al. 2020. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027. find
161. [166] L. Gao, J. Tow, S. Biderman, S. Black, A. DiPofi, C. Foster, L. Golding, J. Hsu et al. 2021. A framework for few-shot language model evaluation. find
162. [173] L. Gong, D. He, Z. Li, T. Qin, L. Wang and T. Liu. 2019. Efficient training of BERT by progressively stacking. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 2337–2346. PMLR. find
163. [174] Z. Gou, Z. Shao, Y. Gong, Y. Shen, Y. Yang, N. Duan and W. Chen. 2023. Critic: Large language models can self-correct with tool-interactive critiquing. arXiv preprint arXiv:2305.11738. find our card
164. [175] K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz and M. Fritz. 2023. More than you’ve asked for: A comprehensive analysis of novel prompt injection threats to application-integrated large language models. arXiv preprint arXiv:2302.12173. find our card
165. [176] L. D. Griffin, B. Kleinberg, M. Mozes, K. T. Mai, M. Vau, M. Caldwell and A. Marvor-Parker. 2023. Susceptibility to influence of large language models. arXiv preprint arXiv:2303.06074. find
166. [177] Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao et al. 2021. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1–23. find our card
167. [178] Y. Gu, S. Zhang, N. Usuyama, Y. Woldesenbet, C. Wong, P. Sanapathi, M. Wei, N. Valluri et al. 2023. Distilling large language models for biomedical knowledge extraction: A case study on adverse drug events. find
168. [179] Y. Gu, X. Han, Z. Liu and M. Huang. 2022. PPT: Pre-trained prompt tuning for few-shot learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410–8423, Dublin, Ireland. Association for Computational Linguistics. find our card
169. [180] A. Gudibande, E. Wallace, C. Snell, X. Geng, H. Liu, P. Abbeel, S. Levine and D. Song. 2023. The false promise of imitating proprietary llms. arXiv preprint arXiv:2305.15717. find our card
170. [181] J. P. Guilford. 1967. Creativity: Yesterday, today and tomorrow. The Journal of Creative Behavior, 1(1):3–14. find
171. [182] S. Gunasekar, Y. Zhang, J. Aneja, C. C. T. Mendes, A. D. Giorno, S. Gopi, M. Javaheripi, P. Kauffmann et al. 2023. Textbooks are all you need. find
172. [183] M. Guo, J. Ainslie, D. Uthus, S. Ontanon, J. Ni, Y.-H. Sung and Y. Yang. 2022. LongT5: Efficient text-to-text transformer for long sequences. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 724–736, Seattle, United States. Association for Computational Linguisti… find our card
173. [184] A. Gupta. 2023. Probing Quantifier Comprehension in Large Language Models. ArXiv:2306.07384 [cs]. — [arXiv:2306.07384](https://arxiv.org/abs/2306.07384) find
174. [185] T. Gupta and A. Kembhavi. 2022. Visual programming: Compositional visual reasoning without training. find
175. [197] D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song and J. Steinhardt. 2021. Measuring massive multi-task language understanding. find
176. [198] D. Hendrycks, N. Carlini, J. Schulman and J. Steinhardt. 2021. Unsolved problems in ml safety. arXiv preprint arXiv:2109.13916. find
177. [199] D. Hendrycks and M. Mazeika. 2022. X-risk analysis for ai research. arXiv preprint arXiv:2206.05862. — [arXiv:2206.05862](https://arxiv.org/abs/2206.05862) find
178. [200] D. Hernandez, T. Brown, T. Conerly, N. DasSarma, D. Drain, S. El-Showk, N. Elhage, Z. Hatfield-Dodds et al. 2022. Scaling laws and interpretability of learning from repeated data. arXiv preprint arXiv:2205.10487. find
179. [201] J. Hestness, S. Narang, N. Ardalani, G. Diamos, H. Jun, H. Kianinejad, M. Patwary, M. Ali et al. 2017. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409. find
180. [186] K. Guu, K. Lee, Z. Tung, P. Pasupat and M. Chang. 2020. Retrieval augmented language model pre-training. In International Conference on Machine Learning, pages 3929–3938. PMLR. find
181. [202] B. L. Hie, V. R. Shanker, D. Xu, T. U. Bruun, P. A. Weidenbacher, S. Tang, W. Wu, J. E. Pak et al. 2023. Efficient evolution of human antibodies from general protein language models. Nature Biotechnology. find
182. [187] J. Haase and P. H. P. Hanel. 2023. Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity. find
183. [203] P. Hingston and M. Preuss. 2011. Red teaming with coevolution. In 2011 IEEE Congress of Evolutionary Computation (CEC), pages 1155–1163. IEEE. find
184. [188] M. Hahn and N. Goyal. 2023. A theory of emergent in-context learning as implicit structure induction. ArXiv, abs/2303.07971. — [arXiv:2303.07971](https://arxiv.org/abs/2303.07971) find
185. [204] J. Ho and T. Salimans. 2022. Classifier-free diffusion guidance. find our card
186. [189] S. Hamilton. 2023. Blind judgement: Agent-based supreme court modelling with gpt. arXiv preprint arXiv:2301.05327. find
187. [190] C. Han, Z. Wang, H. Zhao and H. Ji. 2023. In-context learning of large language models explained as kernel regression. ArXiv, abs/2305.12766. find
188. [191] T. Hartvigsen, S. Sankaranarayanan, H. Palangi, Y. Kim and M. Ghassemi. 2022. Aging with grace: Lifelong model editing with discrete key-value adaptors. arXiv preprint arXiv:2211.11031. find
189. [192] A. Haviv, O. Ram, O. Press, P. Izsak and O. Levy. 2022. Transformer language models without positional encodings still learn positional information. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1382–1390, Abu Dhabi, United Arab Emirates. Association for Compu… find
190. [193] J. Hazell. 2023. Large language models can be used to effectively scale spear phishing campaigns. arXiv preprint arXiv:2305.06972. — [arXiv:2305.06972](https://arxiv.org/abs/2305.06972) find
191. [194] E. L. Henderson, S. J. Westwood and D. J. Simons. 2022. A reproducible systematic map of research on the illusory truth effect. Psychonomic Bulletin & Review, pages 1–24. find
192. [195] P. Henderson, M. S. Krass, L. Zheng, N. Guha, C. D. Manning, D. Jurafsky and D. E. Ho. 2022. Pile of law: Learning responsible data filtering from the law and a 256GB open-source legal dataset. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. find
193. [196] D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song and J. Steinhardt. 2020. Aligning ai with shared human values. arXiv preprint arXiv:2008.02275. find our card
194. [205] J. Hoelscher-Obermaier, J. Persson, E. Kran, I. Konstas and F. Barez. 2023. Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark. ArXiv:2305.17553 [cs]. find
195. [206] J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de las Casas, L. A. Hendricks et al. 2022. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems. find
196. [207] A. Holtzman, J. Buys, L. Du, M. Forbes and Y. Choi. 2020. The curious case of neural text degeneration. In International Conference on Learning Representations. find
197. [208] N. Holzenberger, A. Blair-Stanek and B. Van Durme. 2020. A dataset for statutory reasoning in tax law entailment and question answering. arXiv preprint arXiv:2005.05257. find
198. [209] O. Honovich, T. Scialom, O. Levy and T. Schick. 2022. Unnatural instructions: Tuning language models with (almost) no human labor. arXiv preprint arXiv:2212.09689. find our card
199. [210] S. Hooker. 2021. The hardware lottery. Communications of the ACM, 64(12):58–65. find
200. [211] J. J. Horton. 2023. Large language models as simulated economic agents: What can we learn from homo silicus? arXiv preprint arXiv:2301.07543. — [arXiv:2301.07543](https://arxiv.org/abs/2301.07543) find
201. [212] M. Horton, S. Mehta, A. Farhadi and M. Rastegari. 2023. Bytes Are All You Need: Transformers Operating Directly On File Bytes. ArXiv:2306.00238 [cs]. — [arXiv:2306.00238](https://arxiv.org/abs/2306.00238) find
202. [213] N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan and S. Gelly. 2019. Parameter-efficient transfer learning for nlp. In International Conference on Machine Learning, pages 2790–2799. PMLR. find
203. [214] D. Houser and K. McCabe. 2014. Experimental economics and experimental game theory. In Neuroeconomics, pages 19–34. Elsevier. find
204. [229] HuggingFace. 2023. Huggingchat v0.3.0. Available from: https://huggingface.co/chat. Accessed: 28/06/2023. ↗
205. [215] J. Howard and S. Ruder. 2018. Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 328–339, Melbourne, Australia. Association for Computational Linguistics. find
206. [230] C. Hwang, W. Cui, Y. Xiong, Z. Yang, Z. Liu, H. Hu, Z. Wang, R. Salas et al. 2022. Tutel: Adaptive mixture-of-experts at scale. arXiv preprint arXiv:2206.03382. find
207. [216] S. Hsiao. 2023. What’s ahead for bard: More global, more visual, more integrated. Available from: https://blog.google/technology/ai/ google-bard-updates-io-2023/. Accessed: 28/06/2023. ↗
208. [217] B. Hu, J. Xia, J. Zheng, C. Tan, Y. Huang, Y. Xu and S. Z. Li. 2022. Protein language models and structure prediction: Connection and progression. find
209. [218] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang and W. Chen. 2021. Lora: Low-rank adaptation of large language models. find
210. [219] Z. Hu, Y. Lan, L. Wang, W. Xu, E.-P. Lim, R. K.-W. Lee, L. Bing and S. Poria. 2023. Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933. find our card
211. [220] W. Hua, Z. Dai, H. Liu and Q. Le. 2022. Transformer Quality in Linear Time. In Proceedings of the 39th International Conference on Machine Learning, pages 9099–9117. PMLR. ISSN: 2640-3498. find
212. [221] C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, I. Simon, C. Hawthorne, N. Shazeer, A. M. Dai, M. D. Hoffman et al. 2019. Music transformer. In International Conference on Learning Representations. find
213. [222] J. Huang, S. S. Gu, L. Hou, Y. Wu, X. Wang, H. Yu and J. Han. 2022. Large language models can self-improve. find our card
214. [231] J. P. A. Ioannidis. 2005. Why Most Published Research Findings Are False. PLoS Medicine, 2(8):e124. find
215. [232] D. Ippolito, A. Yuan, A. Coenen and S. Burnam. 2022. Creative writing with an ai-powered writing assistant: Perspectives from professional writers. arXiv preprint arXiv:2211.05030. — [arXiv:2211.05030](https://arxiv.org/abs/2211.05030) find
216. [233] G. Irving, P. Christiano and D. Amodei. 2018. Ai safety via debate. arXiv preprint arXiv:1805.00899. find
217. [234] K. Y. Iu and V. M.-Y. Wong. 2023. Chatgpt by openai: The end of litigation lawyers? Available at SSRN. find
218. [235] S. Iyer, X. V. Lin, R. Pasunuru, T. Mihaylov, D. Simig, P. Yu, K. Shuster, T. Wang et al. 2022. Opt-iml: Scaling language model instruction meta learning through the lens of generalization. find our card
219. [236] G. Izacard, P. Lewis, M. Lomeli, L. Hosseini, F. Petroni, T. Schick, J. Dwivedi-Yu, A. Joulin et al. 2022. Few-shot learning with retrieval augmented language models. arXiv preprint arXiv:2208.03299. find
220. [237] A. Jacovi, A. Caciularu, O. Goldman and Y. Goldberg. 2023. Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks. ArXiv:2305.10160 [cs]. find
221. [238] N. Jain, K. Saifullah, Y. Wen, J. Kirchenbauer, M. Shu, A. Saha, M. Goldblum, J. Geiping et al. 2023. Bring your own data! self-supervised evaluation for large language models. arXiv preprint arXiv:23062.13651. find
222. [223] J. Huang and K. C.-C. Chang. 2023. Towards Reasoning in Large Language Models: A Survey. ArXiv:2212.10403 [cs]. find our card
223. [239] J. Jang, S. Kim, S. Ye, D. Kim, L. Logeswaran, M. Lee, K. Lee and M. Seo. 2023. Exploring the Benefits of Training Expert Language Models over Instruction Tuning. ArXiv:2302.03202 [cs]. find
224. [224] W. Huang, P. Abbeel, D. Pathak and I. Mordatch. 2022. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International Conference on Machine Learning, pages 9118–9147. PMLR. find our card
225. [240] J. R. Jeliazkov, D. del Alamo and J. D. Karpiak. 2023. Esmfold hallucinates native-like protein sequences. bioRxiv. find
226. [225] W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson et al. 2022. Inner monologue: Embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608. find
227. [241] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang et al. 2023. Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12):1– 38. find our card
228. [226] Y. Huang, Y. Cheng, A. Bapna, O. Firat, M. X. Chen, D. Chen, H. Lee, J. Ngiam et al. 2018. Gpipe: Efficient training of giant neural networks using pipeline parallelism. find
229. [242] G. Jiang, M. Xu, S.-C. Zhu, W. Han, C. Zhang and Y. Zhu. 2022. Mpi: Evaluating and inducing personality in pre-trained language models. arXiv preprint arXiv:2206.07550. find
230. [227] Z. Huang, Y. Shen, X. Zhang, J. Zhou, W. Rong and Z. Xiong. 2023. Transformer-patcher: One mistake worth one neuron. In The Eleventh International Conference on Learning Representations. find our card
231. [228] I. Hubara, B. Chmiel, M. Island, R. Banner, J. Naor and D. Soudry. 2021. Accelerated sparse neural training: A provable and efficient method to find n:m transposable masks. In Advances in Neural Information Processing Systems, volume 34, pages 21099–21111. Curran Associates, Inc. find
232. [243] X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang and Q. Liu. 2020. TinyBERT: Distilling BERT for natural language understanding. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4163–4174, Online. Association for Computational Linguistics. find
233. [244] Z. Jin, J. Liu, Z. Lyu, S. Poff, M. Sachan, R. Mihalcea, M. Diab and B. Schölkopf. 2023. Can large language models infer causation from correlation? find
234. [245] A. Jinich, S. Z. Nazia, A. V. Tellez, D. Rappoport, M. AlQuraishi and K. Rhee. 2022. Predicting enzyme substrate chemical structure with protein language models. bioRxiv, pages 2022–09. find
235. [246] Jonathan Frankle [@jefrankle]. 2022. Louder for the people in the back: LARGE MODELS (GPT, DALLE) = DATABASES PROMPTS = QUERIES OUTPUTS = RESPONSES NNs find new relations w/in data. Anyone, no matter the resources, can study better querying langs and possibly beat a big model they could never… find
236. [247] D. Jones. 2022. Development and evaluation of speech recognition for the Welsh language. In Proceedings of the 4th Celtic Language Technology Workshop within LREC2022, pages 52–59, Marseille, France. European Language Resources Association. find
237. [248] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates et al. 2021. Highly accurate protein structure prediction with alphafold. Nature, 596(7873):583–589. find our card
238. [249] J. Kaddour. 2022. Stop wasting my time! saving days of imagenet and bert training with latest weight averaging. arXiv preprint arXiv:2209.14981. find
239. [250] J. Kaddour. 2023. The MiniPile Challenge for Data-Efficient Language Models. ArXiv:2304.08442 [cs]. find
240. [251] J. Kaddour, O. Key, P. Nawrot, P. Minervini and M. J. Kusner. 2023. No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. ArXiv:2307.06440 [cs]. find
241. [252] J. Kaddour, L. Liu, R. Silva and M. Kusner. 2022. When do flat minima optimizers work? In Advances in Neural Information Processing Systems. find
242. [253] J. Kaddour, A. Lynch, Q. Liu, M. J. Kusner and R. Silva. 2022. Causal machine learning: A survey and open problems. arXiv preprint arXiv:2206.15475. — [arXiv:2206.15475](https://arxiv.org/abs/2206.15475) find
243. [254] J. Kaddour, Y. Zhu, Q. Liu, M. J. Kusner and R. Silva. 2021. Causal Effect Inference for Structured Treatments. In Advances in Neural Information Processing Systems, volume 34, pages 24841–24854. Curran Associates, Inc. find
244. [255] M. Kale, A. Siddhant, R. Al-Rfou, L. Xue, N. Constant and M. Johnson. 2021. nmT5 - is parallel data still relevant for pre-training massively multilingual language models? In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Join… find
245. [256] J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford et al. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. find
246. [260] Z. Kenton, T. Everitt, L. Weidinger, I. Gabriel, V. Mikulik and G. Irving. 2021. Alignment of language agents. arXiv preprint arXiv:2103.14659. find
247. [261] N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong and R. Socher. 2019. Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858. find our card
248. [262] O. Khattab, K. Santhanam, X. L. Li, D. Hall, P. Liang, C. Potts and M. Zaharia. 2023. Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP. ArXiv:2212.14024 [cs]. find our card
249. [263] D. Kiela, M. Bartolo, Y. Nie, D. Kaushik, A. Geiger, Z. Wu, B. Vidgen, G. Prasad et al. 2021. Dynabench: Rethinking benchmarking in nlp. arXiv preprint arXiv:2104.14337. find
250. [264] J. Kim, M. Kim and B. Mozafari. 2022. Provable memorization capacity of transformers. In The Eleventh International Conference on Learning Representations. find
251. [265] S. Kim, K. Mangalam, J. Malik, M. W. Mahoney, A. Gholami and K. Keutzer. 2023. Big little transformer decoder. arXiv preprint arXiv:2302.07863. find
252. [266] T. Kim. 2022. Revisiting the practical effectiveness of constituency parse extraction from pre-trained language models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5398–5408, Gyeongju, Republic of Korea. International Committee on Computational Lingu… find
253. [267] L. N. Kinch, R. D. Schaeffer, A. Kryshtafovych and N. V. Grishin. 2021. Target classification in the 14th round of the critical assessment of protein structure prediction (casp14). Proteins: Structure, Function, and Bioinformatics, 89(12):1618–1632. find
254. [268] J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers and T. Goldstein. 2023. A Watermark for Large Language Models. ArXiv:2301.10226 [cs]. find our card
255. [269] J. Kirchenbauer, J. Geiping, Y. Wen, M. Shu, K. Saifullah, K. Kong, K. Fernando, A. Saha et al. 2023. On the Reliability of Watermarks for Large Language Models. ArXiv:2306.04634 [cs]. find
256. [270] R. A. Klein, M. Vianello, F. Hasselman, B. G. Adams, R. B. Adams Jr, S. Alper, M. Aveyard, J. R. Axt et al. 2018. Many labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4):443–490. find
257. [271] D. Kocetkov, R. Li, L. B. Allal, J. Li, C. Mou, C. M. Ferrandis, Y. Jernite, M. Mitchell et al. 2022. The stack: 3 tb of permissively licensed source code. find
258. [257] A. Karpathy. 2023. Tokenization issues (tweet). find
259. [272] J. Kocoń, I. Cichecki, O. Kaszyca, M. Kochanek, D. Szydło, J. Baran, J. Bielaniewicz, M. Gruza et al. 2023. Chatgpt: Jack of all trades, master of none. find
260. [258] D. M. Katz, M. J. Bommarito, S. Gao and P. Arredondo. 2023. Gpt-4 passes the bar exam. Available at SSRN 4389233. find
261. [273] T. Kojima, S. S. Gu, M. Reid, Y. Matsuo and Y. Iwasawa. 2022. Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems. find our card
262. [259] A. Kazemnejad, I. Padhi, K. N. Ramamurthy, P. Das and S. Reddy. 2023. The impact of positional encoding on length generalization in transformers. arXiv preprint arXiv:2305.19466. find
263. [274] A. Köpf, Y. Kilcher, D. von Rütte, S. Anagnostidis, Z.-R. Tam, K. Stevens, A. Barhoum, N. M. Duc et al. 2023. Openassistant conversations–democratizing large language model alignment. arXiv preprint arXiv:2304.07327. find our card
264. [275] T. Korbak, K. Shi, A. Chen, R. Bhalerao, C. L. Buckley, J. Phang, S. R. Bowman and E. Perez. 2023. Pretraining language models with human preferences. arXiv preprint arXiv:2302.08582. find
265. [290] A. Lazaridou, E. Gribovskaya, W. Stokowiec and N. Grigorev. 2022. Internet-augmented language models through few-shot prompting for open-domain question answering. find our card
266. [276] D. M. Korngiebel and S. D. Mooney. 2021. Considering the possibilities and pitfalls of generative pre-trained transformer 3 (gpt-3) in healthcare delivery. NPJ Digital Medicine, 4(1):1–3. find
267. [291] A. Lee, B. Miranda and S. Koyejo. 2023. Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data. ArXiv:2306.13840 [cs]. — [arXiv:2306.13840](https://arxiv.org/abs/2306.13840) find
268. [277] M. Kosinski. 2023. Theory of mind may have spontaneously emerged in large language models. find
269. [292] D. Lee, J. Lee, J.-W. Ha, J.-H. Kim, S.-W. Lee, H. Lee and H. O. Song. 2023. Query-efficient black-box red teaming via bayesian optimization. arXiv preprint arXiv:2305.17444. find
270. [278] B. Krause, A. D. Gotmare, B. McCann, N. S. Keskar, S. Joty, R. Socher and N. F. Rajani. 2021. GeDi: Generative discriminator guided sequence generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4929–4952, Punta Cana, Dominican Republic. Association for Co… find our card
271. [279] D. C. Krawczyk. 2018. Introduction to reasoning. Reasoning—The Neuroscience of How We Think; Academic Press: Cambridge, MA, USA, pages 1–11. find
272. [280] K. Krishna, Y. Song, M. Karpinska, J. Wieting and M. Iyyer. 2023. Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. ArXiv:2303.13408 [cs]. find our card
273. [281] T. Kudo. 2018. Subword regularization: Improving neural network translation models with multiple subword candidates. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 66–75, Melbourne, Australia. Association for Computatio… find
274. [282] T. Kudo and J. Richardson. 2018. Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226. find our card
275. [283] A. Kulkarni. 2021. GitHub Copilot AI Is Leaking Functional API Keys. find
276. [293] K. Lee, O. Firat, A. Agarwal, C. Fannjiang and D. Sussillo. 2018. Hallucinations in neural machine translation. find
277. [294] K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch and N. Carlini. 2021. Deduplicating training data makes language models better. arXiv preprint arXiv:2107.06499. find
278. [295] N. Lee, W. Ping, P. Xu, M. Patwary, P. Fung, M. Shoeybi and B. Catanzaro. Factuality Enhanced Language Models for Open-Ended Text Generation. [296] P. Lee, S. Bubeck and J. Petro. 2023. Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine. New England Journal of Medicine, 388(13)… find
279. [297] E. Lehman, E. Hernandez, D. Mahajan, J. Wulff, M. J. Smith, Z. Ziegler, D. Nadler, P. Szolovits et al. 2023. Do we still need clinical language models? find
280. [298] D. Lepikhin, H. Lee, Y. Xu, D. Chen, O. Firat, Y. Huang, M. Krikun, N. Shazeer et al. 2020. Gshard: Scaling giant models with conditional computation and automatic sharding. find
281. [299] B. Lester, R. Al-Rfou and N. Constant. 2021. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic. Association for Computational Linguistic… find
282. [284] S. R. Künzel, J. S. Sekhon, P. J. Bickel and B. Yu. 2019. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences, 116(10):4156–4165. find
283. [300] Y. Leviathan, M. Kalman and Y. Matias. 2022. Fast inference from transformers via speculative decoding. arXiv preprint arXiv:2211.17192. find
284. [285] W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. Yu, J. Gonzalez, H. Zhang et al. 2023. vllm: Easy, fast, and cheap llm serving with pagedattention. find
285. [301] D. M. Levine, R. Tuwani, B. Kompa, A. Varma, S. G. Finlayson, A. Mehrotra and A. Beam. 2023. The diagnostic and triage accuracy of the gpt-3 artificial intelligence model. medRxiv, pages 2023–01. find
286. [286] E. Kıcıman, R. Ness, A. Sharma and C. Tan. 2023. Causal reasoning and large language models: Opening a new frontier for causality. find
287. [302] M. Lewis, S. Bhosale, T. Dettmers, N. Goyal and L. Zettlemoyer. 2021. Base layers: Simplifying training of large, sparse models. find
288. [287] P. Lab. 2023. Awesome-Prompt-Engineering. Original-date: 2023-02-09T18:22:52Z. find
289. [288] A. K. Lampinen, S. C. Chan, I. Dasgupta, A. J. Nam and J. X. Wang. 2023. Passive learning of active causal strategies in agents and language models. arXiv preprint arXiv:2305.16183. — [arXiv:2305.16183](https://arxiv.org/abs/2305.16183) find
290. [303] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov and L. Zettlemoyer. 2020. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computati… find our card
291. [289] H. Laurençon, L. Saulnier, T. Wang, C. Akiki, A. V. del Moral, T. L. Scao, L. V. Werra, C. Mou et al. 2022. The bigscience ROOTS corpus: A 1.6TB composite multilingual dataset. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. find
292. [304] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474. find
293. [305] A. Lewkowycz, A. Andreassen, D. Dohan, E. Dyer, H. Michalewski, V. Ramasesh, A. Slone, C. Anil et al. 2022. Solving quantitative reasoning problems with language models. find our card
294. [321] C.-C. Lin, A. Jaech, X. Li, M. R. Gormley and J. Eisner. 2020. Limitations of autoregressive models and their alternatives. arXiv preprint arXiv:2010.11939. find
295. [306] B. Z. Li, M. Nye and J. Andreas. 2021. Implicit representations of meaning in neural language models. arXiv preprint arXiv:2106.00737. find
296. [322] J. Lin, A. Yang, J. Bai, C. Zhou, L. Jiang, X. Jia, A. Wang, J. Zhang et al. 2021. M6-10t: A sharing-delinking paradigm for efficient multi-trillion parameter pretraining. arXiv preprint arXiv:2110.03888. find
297. [307] C. Li, A. A. Awan, H. Tang, S. Rajbhandari and Y. He. 2021. 1-bit lamb: Communication efficient large-scale large-batch training with lamb’s convergence speed. arXiv preprint arXiv:2104.06069. find
298. [323] S. Lin, J. Hilton and O. Evans. 2021. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958. find our card
299. [308] D. Li, R. Shao, A. Xie, Y. Sheng, L. Zheng, J. E. Gonzalez, I. Stoica, X. Ma et al. 2023. How long can open-source llms truly promise on context length? find
300. [324] X. V. Lin, T. Mihaylov, M. Artetxe, T. Wang, S. Chen, D. Simig, M. Ott, N. Goyal et al. 2022. Few-shot learning with multilingual generative language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9019–9052, Abu Dhabi, United Arab Emira… find our card
301. [309] H. Li, D. Guo, W. Fan, M. Xu and Y. Song. 2023. Multi-step jailbreaking privacy attacks on chatgpt. arXiv preprint arXiv:2304.05197. find our card
302. [310] R. Li, J. Su, C. Duan and S. Zheng. 2020. Linear attention mechanism: An efficient attention for semantic segmentation. arXiv preprint arXiv:2007.14902. — [arXiv:2007.14902](https://arxiv.org/abs/2007.14902) find
303. [311] X. L. Li and P. Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582–45… find
304. [312] Y. Li, Z. Lin, S. Zhang, Q. Fu, B. Chen, J.-G. Lou and W. Chen. 2022. On the advance of making language models better reasoners. find
305. [313] Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling et al. 2022. Competition-level code generation with alphacode. Science, 378(6624):1092–1097. find our card
306. [314] Z. Li, C. You, S. Bhojanapalli, D. Li, A. S. Rawat, S. J. Reddi, K. Ye, F. Chern et al. 2023. The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers. ArXiv:2210.06313 [cs, stat]. — [arXiv:2210.06313](https://arxiv.org/abs/2210.06313) find
307. [315] L. Lian, B. Li, A. Yala and T. Darrell. 2023. Llm-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models. find
308. [316] J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence and A. Zeng. 2023. Code as policies: Language model programs for embodied control. find
309. [317] P. P. Liang, C. Wu, L.-P. Morency and R. Salakhutdinov. 2021. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning, pages 6565–6576. PMLR. find our card
310. [318] P. Liang, R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y. Zhang, D. Narayanan et al. 2022. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110. find
311. [325] Y.-T. Lin and Y.-N. Chen. 2023. Llm-eval: Unified multi-dimensional automatic evaluation for open-domain conversations with large language models. arXiv preprint arXiv:2305.13711. find our card
312. [326] Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, A. dos Santos Costa, M. Fazel-Zarandi et al. 2022. Language models of protein sequences at the scale of evolution enable accurate structure prediction. BioRxiv. find
313. [327] W. Ling, D. Yogatama, C. Dyer and P. Blunsom. 2017. Program induction by rationale generation: Learning to solve and explain algebraic word problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 158–167, Vancouver, Ca… find
314. [328] B. Liu, J. T. Ash, S. Goel, A. Krishnamurthy and C. Zhang. 2023. Exposing Attention Glitches with Flip-Flop Language Modeling. ArXiv:2306.00946 [cs]. find
315. [329] F. Liu, J. M. Eisenschlos, F. Piccinno, S. Krichene, C. Pang, K. Lee, M. Joshi, W. Chen et al. 2022. Deplot: One-shot visual language reasoning by plot-to-table translation. arXiv preprint arXiv:2212.10505. find
316. [330] H. Liu, C. Sferrazza and P. Abbeel. 2023. Languages are rewards: Hindsight finetuning using human feedback. arXiv preprint arXiv:2302.02676. find
317. [331] H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal and C. A. Raffel. 2022. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Advances in Neural Information Processing Systems, 35:1950–1965. find
318. [332] H. Liu, S. M. Xie, Z. Li and T. Ma. 2022. Same pre-training loss, better downstream: Implicit bias matters for language models. ArXiv, abs/2210.14199. — [arXiv:2210.14199](https://arxiv.org/abs/2210.14199) find
319. [319] O. Lieber, O. Sharir, B. Lenz and Y. Shoham. 2021. Jurassic-1: Technical details and evaluation. White Paper. AI21 Labs, 1. find
320. [333] N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni and P. Liang. 2023. Lost in the Middle: How Language Models Use Long Contexts. ArXiv:2307.03172 [cs]. find our card
321. [320] V. Liévin, C. E. Hother and O. Winther. 2022. Can large language models reason about medical questions? arXiv preprint arXiv:2207.08143. — [arXiv:2207.08143](https://arxiv.org/abs/2207.08143) find
322. [334] R. Liu, C. Jia, J. Wei, G. Xu and S. Vosoughi. 2022. Quantifying and alleviating political bias in language models. Artificial Intelligence, 304:103654. find
323. [335] R. Liu and N. B. Shah. 2023. ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing. ArXiv:2306.00622 [cs]. find
324. [336] S. Liu and Z. Wang. 2023. Ten lessons we have learned in the new" sparseland": A short handbook for sparse neural network researchers. arXiv preprint arXiv:2302.02596. — [arXiv:2302.02596](https://arxiv.org/abs/2302.02596) find
325. [337] X. Liu, X. Yang, L. Ouyang, G. Guo, J. Su, R. Xi, K. Yuan and F. Yuan. 2022. Protein language model predicts mutation pathogenicity and clinical prognosis. bioRxiv, pages 2022–09. find
326. [338] Z. Liu, A. Bahety and S. Song. 2023. Reflect: Summarizing robot experiences for failure explanation and correction. find
327. [339] Z. Liu, E. Gan and M. Tegmark. 2023. Seeing is believing: Brain-inspired modular training for mechanistic interpretability. arXiv preprint arXiv:2305.08746. — [arXiv:2305.08746](https://arxiv.org/abs/2305.08746) find
328. [340] S. Longpre, L. Hou, T. Vu, A. Webson, H. W. Chung, Y. Tay, D. Zhou, Q. V. Le et al. 2023. The flan collection: Designing data and methods for effective instruction tuning. find our card
329. [341] S. Longpre, G. Yauney, E. Reif, K. Lee, A. Roberts, B. Zoph, D. Zhou, J. Wei et al. 2023. A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity. ArXiv:2305.13169 [cs]. find
330. [342] Y. Lu, M. Bartolo, A. Moore, S. Riedel and P. Stenetorp. 2022. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8086–8098… find our card
331. [343] Y. Lu, C. Li, M. Zhang, C. De Sa and Y. He. 2022. Maximizing communication efficiency for large-scale training via 0/1 adam. arXiv preprint arXiv:2202.06009. find
332. [344] N. Lukas, A. Salem, R. Sim, S. Tople, L. Wutschitz and S. Zanella-Béguelin. 2023. Analyzing Leakage of Personally Identifiable Information in Language Models. ArXiv:2302.00539 [cs]. find
333. [345] B. Luo, R. Y. Lau, C. Li and Y.-W. Si. 2022. A critical review of state-of-the-art chatbot designs and applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(1):e1434. find
334. [346] Y. Luo, N. Tang, G. Li, C. Chai, W. Li and X. Qin. 2021. Synthesizing natural language to visualization (nl2vis) benchmarks from nl2sql benchmarks. In Proceedings of the 2021 International Conference on Management of Data, pages 1235–1247. find
335. [347] A. Lynch, G. J. Dovonon, J. Kaddour and R. Silva. 2023. Spawrious: A benchmark for fine control of spurious correlation biases. arXiv preprint arXiv:2303.05470. — [arXiv:2303.05470](https://arxiv.org/abs/2303.05470) find
336. [348] P. Ma, Z. Li, A. Sun and S. Wang. 2023. "oops, did i just say that?" testing and repairing unethical suggestions of large language models with suggest-critique-reflect process. arXiv preprint arXiv:2305.02626. — [arXiv:2305.02626](https://arxiv.org/abs/2305.02626) find
337. [349] X. Ma, G. Fang and X. Wang. 2023. Llm-pruner: On the structural pruning of large language models. arXiv preprint arXiv:2305.11627. find our card
338. [350] X. Ma, X. Kong, S. Wang, C. Zhou, J. May, H. Ma and L. Zettlemoyer. 2021. Luna: Linear unified nested attention. Advances in Neural Information Processing Systems, 34:2441–2453. find
339. [351] A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri et al. 2023. Self-refine: Iterative refinement with self-feedback. find
340. [352] A. Madani, B. Krause, E. R. Greene, S. Subramanian, B. P. Mohr, J. M. Holton, J. L. Olmos Jr, C. Xiong et al. 2023. Large language models generate functional protein sequences across diverse families. Nature Biotechnology, pages 1–8. find
341. [353] M. Maddela, M. Ung, J. Xu, A. Madotto, H. Foran and Y.-L. Boureau. 2023. Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts. ArXiv:2307.02768 [cs]. find
342. [354] S. Mahdavi, R. Liao and C. Thrampoulidis. 2023. Memorization Capacity of Multi-Head Attention in Transformers. ArXiv:2306.02010 [cs]. — [arXiv:2306.02010](https://arxiv.org/abs/2306.02010) find
343. [355] S. Malladi, T. Gao, E. Nichani, A. Damian, J. D. Lee, D. Chen and S. Arora. 2023. Fine-Tuning Language Models with Just Forward Passes. ArXiv:2305.17333 [cs]. find
344. [356] S. Mangrulkar, S. Gugger, L. Debut, Y. Belkada and S. Paul. 2022. Peft: State-of-the-art parameter-efficient fine-tuning methods. https://github.com/ huggingface/peft. ↗
345. [357] P. Maniatis and D. Tarlow. 2023. Large sequence models for software development activities. Available from: https://ai.googleblog.com/2023/ 05/large-sequence-models-for-software. html. Accessed: 26/06/2023. ↗
346. [358] R. R. McCrae and P. T. Costa Jr. 1997. Personality trait structure as a human universal. American psychologist, 52(5):509. find
347. [359] I. R. McKenzie, A. Lyzhov, M. Pieler, A. Parrish, A. Mueller, A. Prabhu, E. McLean, A. Kirtland et al. 2023. Inverse Scaling: When Bigger Isn’t Better. ArXiv:2306.09479 [cs]. find
348. [360] K. Meng, D. Bau, A. J. Andonian and Y. Belinkov. 2022. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems. find
349. [361] K. Meng, A. S. Sharma, A. J. Andonian, Y. Belinkov and D. Bau. 2023. Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations. find
350. [362] J. Menick, M. Trebacz, V. Mikulik, J. Aslanides, F. Song, M. Chadwick, M. Glaese, S. Young et al. 2022. Teaching language models to support answers with verified quotes. find our card
351. [363] G. Mialon, R. Dessì, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Rozière, T. Schick et al. 2023. Augmented language models: a survey. arXiv preprint arXiv:2302.07842. find our card
352. [364] S. Milgram. 1963. Behavioral study of obedience. The Journal of abnormal and social psychology, 67(4):371. find
353. [365] S. Min, K. Krishna, X. Lyu, M. Lewis, W.-t. Yih, P. W. Koh, M. Iyyer, L. Zettlemoyer et al. 2023. FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. ArXiv:2305.14251 [cs]. find our card
354. [366] S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi and L. Zettlemoyer. 2022. Rethinking the role of demonstrations: What makes in-context learning work? find our card
355. [380] N. Nanda, L. Chan, T. Lieberum, J. Smith and J. Steinhardt. 2023. Progress measures for grokking via mechanistic interpretability. In The Eleventh International Conference on Learning Representations. find our card
356. [367] M. Miotto, N. Rossberg and B. Kleinberg. 2022. Who is gpt-3? an exploration of personality, values and demographics. arXiv preprint arXiv:2209.14338. find
357. [381] S. Nerella, S. Bandyopadhyay, J. Zhang, M. Contreras, S. Siegel, A. Bumin, B. Silva, J. Sena et al. 2023. Transformers in healthcare: A survey. find
358. [368] P. Mirowski, K. W. Mathewson, J. Pittman and R. Evans. 2022. Co-writing screenplays and theatre scripts with language models: An evaluation by industry professionals. arXiv preprint arXiv:2209.14958. — [arXiv:2209.14958](https://arxiv.org/abs/2209.14958) find
359. [382] A. Nguyen, N. Karampatziakis and W. Chen. 2023. Meet in the middle: A new pre-training paradigm. arXiv preprint arXiv:2303.07295. — [arXiv:2303.07295](https://arxiv.org/abs/2303.07295) find
360. [369] A. Mishra, J. A. Latorre, J. Pool, D. Stosic, D. Stosic, G. Venkatesh, C. Yu and P. Micikevicius. 2021. Accelerating sparse deep neural networks. arXiv preprint arXiv:2104.08378. find
361. [370] S. Mishra, D. Khashabi, C. Baral and H. Hajishirzi. 2022. Cross-task generalization via natural language crowdsourcing instructions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3470–3487, Dublin, Ireland. Association… find
362. [371] E. Mitchell, Y. Lee, A. Khazatsky, C. D. Manning and C. Finn. 2023. DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. ArXiv:2301.11305 [cs]. find our card
363. [372] E. Mitchell, C. Lin, A. Bosselut, C. Finn and C. D. Manning. 2022. Fast model editing at scale. In International Conference on Learning Representations. find
364. [373] E. Mitchell, C. Lin, A. Bosselut, C. D. Manning and C. Finn. 2022. Memory-based model editing at scale. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 15817–15831. PMLR. find
365. [374] R. Moriconi, M. P. Deisenroth and K. Sesh Kumar. 2020. High-dimensional bayesian optimization using low-dimensional feature spaces. Machine Learning, 109:1925– 1943. find
366. [375] M. Moussaïd, J. E. Kämmer, P. P. Analytis and H. Neth. 2013. Social influence and the collective dynamics of opinion formation. PloS one, 8(11):e78433. find
367. [376] M. Mozes, J. Hoffmann, K. Tomanek, M. Kouate, N. Thain, A. Yuan, T. Bolukbasi and L. Dixon. 2023. Towards agile text classifiers for everyone. arXiv preprint arXiv:2302.06541. find
368. [377] N. Muennighoff, T. Wang, L. Sutawika, A. Roberts, S. Biderman, T. L. Scao, M. S. Bari, S. Shen et al. 2022. Crosslingual generalization through multitask finetuning. arXiv preprint arXiv:2211.01786. find our card
369. [378] S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi and A. Awadallah. 2023. Orca: Progressive learning from complex explanation traces of gpt-4. arXiv preprint arXiv:2306.02707. find our card
370. [379] R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain et al. 2021. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332. find our card
371. [383] E. Nguyen, M. Poli, M. Faizi, A. Thomas, C. Birch-Sykes, M. Wornow, A. Patel, C. Rabideau et al. 2023. Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution. arXiv preprint arXiv:2306.15794. — [arXiv:2306.15794](https://arxiv.org/abs/2306.15794) find
372. [384] A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever and M. Chen. 2022. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. find
373. [385] X. Nie and S. Wager. 2021. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2):299– 319. find
374. [386] E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese and C. Xiong. 2022. Codegen: An open large language model for code with multi-turn program synthesis. find
375. [387] F. Niu, B. Recht, C. Re, S. J. Wright and W. D. St. Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. [388] H. Nori, N. King, S. M. McKinney, D. Carignan and E. Horvitz. 2023. Capabilities of gpt-4 on medical challenge problems. find our card
376. [389] K. Nottingham, P. Ammanabrolu, A. Suhr, Y. Choi, H. Hajishirzi, S. Singh and R. Fox. 2023. Do embodied agents dream of pixelated sheep?: Embodied decision making using language guided world modelling. arXiv preprint arXiv:2301.12050. — [arXiv:2301.12050](https://arxiv.org/abs/2301.12050) find
377. [390] S. Nurk, S. Koren, A. Rhie, M. Rautiainen, A. V. Bzikadze, A. Mikheenko, M. R. Vollger, N. Altemose et al. 2022. The complete sequence of a human genome. Science, 376(6588):44–53. find
378. [391] M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz et al. 2021. Show your work: Scratchpads for intermediate computation with language models. find our card
379. [392] Ofir Press [@OfirPress]. 2022. GPT-3 seems to be nondeterministic even when it should be (i.e. temperature == 0). Has anyone else noticed this? Is there a known fix? Video by my collaborator Muru Zhang. https://t.co/dOWYWPBYyP. ↗
380. [393] N. Oh, G.-S. Choi and W. Y. Lee. 2023. Chatgpt goes to operating room: Evaluating gpt-4 performance and its potential in surgical education and training in the era of large language models. medRxiv. find
381. [394] C. Olah. Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases. [395] C. Olsson, N. Elhage, N. Nanda, N. Joseph, N. Das-Sarma, T. Henighan, B. Mann, A. Askell et al. 2022. In-context learning and induction heads. arXiv preprint arXiv:2209.11895. find
382. [396] OpenAI. 2022. Chatgpt: Optimizing language models for dialogue. https://openai.com/blog/ chatgpt/. Accessed: 2023-02-18. ↗
383. [397] OpenAI. 2023. Chat gpt 4 painfully slow. https://community.openai.com/t/ chat-gpt-4-painfully-slow/117996. ↗
384. [398] OpenAI. 2023. Gpt-4 technical report. find our card
385. [399] P. J. Ortiz Su’arez, B. Sagot and L. Romary. 2019. Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures. In Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019, pages 9 – 16, Mannheim. Leibn… find
386. [400] M. Ott, S. Edunov, A. Baevski, A. Fan, S. Gross, N. Ng, D. Grangier and M. Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. arXiv preprint arXiv:1904.01038. find our card
387. [411] N. D. Patson, E. S. Darowski, N. Moon and F. Ferreira. 2009. Lingering misinterpretations in garden-path sentences: evidence from a paraphrasing task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(1):280. find
388. [412] D. Patterson, J. Gonzalez, U. Hölzle, Q. Le, C. Liang, L.-M. Munguia, D. Rothchild, D. R. So et al. 2022. The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7):18–28. find
389. [413] A. Paullada, I. D. Raji, E. M. Bender, E. Denton and A. Hanna. 2021. Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns, 2(11):100336. find
390. [414] M. Pellert, C. M. Lechner, C. Wagner, B. Rammstedt and M. Strohmaier. 2023. Ai psychometrics: Using psychometric inventories to obtain psychological profiles of large language models. find
391. [415] G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei et al. 2023. The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only. ArXiv:2306.01116 [cs]. find
392. [401] N. Ousidhoum, X. Zhao, T. Fang, Y. Song and D.-Y. Yeung. 2021. Probing toxic content in large pre-trained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volu… find
393. [416] B. Peng, E. Alcaide, Q. Anthony, A. Albalak, S. Arcadinho, H. Cao, X. Cheng, M. Chung et al. 2023. RWKV: Reinventing RNNs for the Transformer Era. ArXiv:2305.13048 [cs]. find
394. [402] C. Outeiral and C. Deane. 2022. Codon language embeddings provide strong signals for protein engineering. bioRxiv, pages 2022–12. find
395. [417] B. Peng, E. Alcaide, Q. Anthony, A. Albalak, S. Arcadinho, H. Cao, X. Cheng, M. Chung et al. 2023. Rwkv: Reinventing rnns for the transformer era. arXiv preprint arXiv:2305.13048. find
396. [403] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal et al. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems. find
397. [404] M. Pagliardini, D. Paliotta, M. Jaggi and F. Fleuret. 2023. Faster causal attention over large sequences through sparse flash attention. find
398. [418] C. Peng, X. Yang, A. Chen, K. E. Smith, N. PourNejatian, A. B. Costa, C. Martin, M. G. Flores et al. 2023. A study of generative large language model for medical research and healthcare. find
399. [405] J. Pan, T. Gao, H. Chen and D. Chen. 2023. What in-context learning "learns" in-context: Disentangling task recognition and task learning. find
400. [419] Y. Peng. 2021. A MARVS analysis of two Chinese near-synonymous verbs of jumping based on Chinese corpora. In Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, pages 483–492, Shanghai, China. Association for Computational Lingustics. find
401. [406] B. Paranjape, S. Lundberg, S. Singh, H. Hajishirzi, L. Zettlemoyer and M. T. Ribeiro. 2023. Art: Automatic multi-step reasoning and tool-use for large language models. find
402. [420] E. Perez, S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese et al. 2022. Red teaming language models with language models. arXiv preprint arXiv:2202.03286. find our card
403. [407] G. Park, B. Park, S. J. Kwon, B. Kim, Y. Lee and D. Lee. 2022. nuqmm: Quantized matmul for efficient inference of large-scale generative language models. arXiv preprint arXiv:2206.09557. find
404. [421] E. Perez, S. Ringer, K. Lukošiūtė, K. Nguyen, E. Chen, S. Heiner, C. Pettit, C. Olsson et al. 2022. Discovering language model behaviors with model-written evaluations. find our card
405. [408] J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang and M. S. Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. find
406. [422] F. Perez and I. Ribeiro. 2022. Ignore previous prompt: Attack techniques for language models. arXiv preprint arXiv:2211.09527. find our card
407. [409] P. S. Park, P. Schoenegger and C. Zhu. 2023. Artificial intelligence in psychology research. arXiv preprint arXiv:2302.07267. — [arXiv:2302.07267](https://arxiv.org/abs/2302.07267) find
408. [423] L. Peric, S. Mijic, D. Stammbach and E. Ash. 2020. Legal language modeling with transformers. In Proceedings of the Fourth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2020) held online in conjunction with te 33rd International Conference on Legal Knowledge and I… find
409. [410] A. Patel, B. Li, M. S. Rasooli, N. Constant, C. Raffel and C. Callison-Burch. 2023. Bidirectional language models are also few-shot learners. find
410. [424] B. Peters and A. F. T. Martins. 2021. Smoothing and shrinking the sparse Seq2Seq search space. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2642–2654, Online. Association for Computation… find
411. [425] J. Peters, D. Janzing and B. Schölkopf. 2017. Elements of causal inference: foundations and learning algorithms. The MIT Press. find
412. [426] A. Petrov, E. La Malfa, P. H. Torr and A. Bibi. 2023. Language model tokenizers introduce unfairness between languages. arXiv preprint arXiv:2305.15425. find
413. [427] T. Pettinato Oltz. 2023. Chatgpt, professor of law. Professor of Law (February 4, 2023). find
414. [428] J. Pfeiffer, A. Rücklé, C. Poth, A. Kamath, I. Vulić, S. Ruder, K. Cho and I. Gurevych. 2020. AdapterHub: A framework for adapting transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 46– 54, Online. Association… find
415. [429] S. Pichai. 2023. An important next step on our ai journey. https://blog.google/technology/ai/ bard-google-ai-search-updates/. Accessed: 2023-02-18. ↗
416. [430] M. Poli, S. Massaroli, E. Nguyen, D. Y. Fu, T. Dao, S. Baccus, Y. Bengio, S. Ermon et al. 2023. Hyena Hierarchy: Towards Larger Convolutional Language Models. ArXiv:2302.10866 [cs]. find
417. [431] R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, A. Levskaya, J. Heek, K. Xiao et al. 2022. Efficiently Scaling Transformer Inference. ArXiv:2211.05102 [cs]. find
418. [432] R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, A. Levskaya, J. Heek, K. Xiao et al. 2022. Efficiently scaling transformer inference. arXiv preprint arXiv:2211.05102. find
419. [433] V. Prabhakaran, A. Mostafazadeh Davani and M. Diaz. 2021. On releasing annotator-level labels and information in datasets. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 133–138, Punta Cana, Dominican Republ… find
420. [434] O. Press, N. A. Smith and M. Lewis. 2021. Train short, test long: Attention with linear biases enables input length extrapolation. find
421. [435] O. Press, M. Zhang, S. Min, L. Schmidt, N. A. Smith and M. Lewis. 2023. Measuring and Narrowing the Compositionality Gap in Language Models. ArXiv:2210.03350 [cs]. find
422. [436] J. Qian, H. Wang, Z. Li, S. Li and X. Yan. 2022. Limitations of language models in arithmetic and symbolic induction. arXiv preprint arXiv:2208.05051. find
423. [437] J. Rabelo, R. Goebel, M.-Y. Kim, Y. Kano, M. Yoshioka and K. Satoh. 2022. Overview and discussion of the competition on legal information Extraction/Entailment (COLIEE) 2021. The Review of Socionetwork Strategies, 16(1):111–133. find
424. [438] A. Radford, R. Jozefowicz and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444. find our card
425. [439] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey and I. Sutskever. 2022. Robust Speech Recognition via Large-Scale Weak Supervision. ArXiv:2212.04356 [cs, eess]. find our card
426. [440] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei and I. Sutskever. 2019. Language models are unsupervised multitask learners. find
427. [441] J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson et al. 2021. Scaling language models: Methods, analysis & insights from training gopher. arXiv preprint arXiv:2112.11446. find
428. [442] R. Rafailov, A. Sharma, E. Mitchell, S. Ermon, C. D. Manning and C. Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290. find
429. [443] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li et al. 2022. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(1). find
430. [444] S. Rajbhandari, C. Li, Z. Yao, M. Zhang, R. Y. Aminabadi, A. A. Awan, J. Rasley and Y. He. 2022. DeepSpeed-MoE: Advancing mixture-of-experts inference and training to power next-generation AI scale. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedi… find our card
431. [445] S. Rajbhandari, J. Rasley, O. Ruwase and Y. He. 2020. Zero: Memory optimizations toward training trillion parameter models. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’20. IEEE Press. find
432. [446] S. Rajbhandari, O. Ruwase, J. Rasley, S. Smith and Y. He. 2021. Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’21, New York, NY, USA. Association f… find
433. [447] I. D. Raji, E. M. Bender, A. Paullada, E. Denton and A. Hanna. 2021. Ai and the everything in the whole wide world benchmark. arXiv preprint arXiv:2111.15366. find
434. [448] A. Rajkomar, E. Loreaux, Y. Liu, J. Kemp, B. Li, M.-J. Chen, Y. Zhang, A. Mohiuddin et al. 2022. Deciphering clinical abbreviations with a privacy protecting machine learning system. Nature Communications, 13(1):7456. find
435. [449] R. Ramamurthy, P. Ammanabrolu, K. Brantley, J. Hessel, R. Sifa, C. Bauckhage, H. Hajishirzi and Y. Choi. 2022. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241. find our card
436. [450] J. Rasley, S. Rajbhandari, O. Ruwase and Y. He. 2020. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, page 3505–3506, New York, N… find
437. [451] P. P. Ray. 2023. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3:121–154. find
438. [452] E. Razumovskaia, J. Maynez, A. Louis, M. Lapata and S. Narayan. 2022. Little red riding hood goes around the globe: Crosslingual story planning and generation with large language models. arXiv preprint arXiv:2212.10471. find
439. [453] B. Recht, C. Re, S. Wright and F. Niu. 2011. Hogwild!: A lock-free approach to parallelizing stochastic gradient descent. Advances in neural information processing systems, 24. find
440. [454] J. Ren, S. Rajbhandari, R. Y. Aminabadi, O. Ruwase, S. Yang, M. Zhang, D. Li and Y. He. 2021. {ZeRO-Offload}: Democratizing {Billion-Scale} model training. In 2021 USENIX Annual Technical Conference (USENIX ATC 21), pages 551–564. find our card
441. [455] X. Ren, P. Zhou, X. Meng, X. Huang, Y. Wang, W. Wang, P. Li, X. Zhang et al. 2023. Pangu-Sigma: Towards trillion parameter language model with sparse heterogeneous computing. find
442. [456] Riley Goodside [@goodside]. 2022. An edge-case in GPT-3 with big implications: Inference is non-deterministic (even at temperature=0) when top-2 token probabilities are \<1% different. So temperature=0 output is *very close* to deterministic, but actually isn’t. Worth remembering. find
443. [457] X. Robin, J. Haas, R. Gumienny, A. Smolinski, G. Tau-riello and T. Schwede. 2021. Continuous automated model evaluation (cameo)—perspectives on the future of fully automated evaluation of structure prediction methods. Proteins: Structure, Function, and Bioinformatics, 89(12):1977–1986. find
444. [458] A. Rohrbach, L. A. Hendricks, K. Burns, T. Darrell and K. Saenko. 2018. Object hallucination in image captioning. arXiv preprint arXiv:1809.02156. find our card
445. [459] S. Roller, S. Sukhbaatar, A. Szlam and J. Weston. 2021. Hash layers for large sparse models. find
446. [460] G. M. Rosa, L. Bonifacio, V. Jeronymo, H. Abonizio, R. Lotufo and R. Nogueira. 2022. Billions of parameters are worth more than in-domain training data: A case study in the legal case entailment task. arXiv preprint arXiv:2205.15172. find
447. [461] L. Ross, D. Greene and P. House. 1977. The “false consensus effect”: An egocentric bias in social perception and attribution processes. Journal of experimental social psychology, 13(3):279–301. find
448. [462] Y. Rottenstreich and C. K. Hsee. 2001. Money, kisses, and electric shocks: On the affective psychology of risk. Psychological science, 12(3):185–190. find
449. [463] A. Roush. You probably don’t know how to do Prompt Engineering, let me educate you. [464] L. Ruis, A. Khan, S. Biderman, S. Hooker, T. Rocktäschel and E. Grefenstette. 2022. Large language models are not zero-shot communicators. find
450. [465] J. Rumbelow and mwatkins. SolidGoldMagikarp (plus, prompt generation). [466] S. Russell. 2021. Human-compatible artificial intelligence. Human-like machine intelligence, pages 3–23. find
451. [467] P. Rust, J. F. Lotz, E. Bugliarello, E. Salesky, M. de Lhoneux and D. Elliott. 2023. Language Modelling with Pixels. ArXiv:2207.06991 [cs]. — [arXiv:2207.06991](https://arxiv.org/abs/2207.06991) find
452. [468] A. Sabne. 2020. Xla : Compiling machine learning for peak performance. find
453. [469] V. S. Sadasivan, A. Kumar, S. Balasubramanian, W. Wang and S. Feizi. 2023. Can AI-Generated Text be Reliably Detected? ArXiv:2303.11156 [cs]. find our card
454. [470] M. Safdari, G. Serapio-García, C. Crepy, S. Fitz, P. Romero, L. Sun, M. Abdulhai, A. Faust et al. 2023. Personality traits in large language models. find our card
455. [471] S. Sagawa, P. W. Koh, T. B. Hashimoto and P. Liang. 2020. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. find
456. [472] O. Sainz, J. C. Campos, I. García-Ferrero, J. Etxaniz and E. Agirre. lm-contamination. [473] L. Salewski, S. Alaniz, I. Rio-Torto, E. Schulz and Z. Akata. 2023. In-context impersonation reveals large language models’ strengths and biases. arXiv preprint arXiv:2305.14930. — [arXiv:2305.14930](https://arxiv.org/abs/2305.14930) find
457. [474] G. Sanchez, H. Fan, A. Spangher, E. Levi, P. S. Ammanamanchi and S. Biderman. 2023. Stay on topic with Classifier-Free Guidance. ArXiv:2306.17806 [cs]. — [arXiv:2306.17806](https://arxiv.org/abs/2306.17806) find
458. [475] V. Sanh, A. Webson, C. Raffel, S. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler et al. 2022. Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations. find our card
459. [476] S. Sanyal, J. Kaddour, A. Kumar and S. Sanghavi. 2023. Understanding the effectiveness of early weight averaging for training large language models. find
460. [477] E. Saravia. 2022. Prompt Engineering Guide. Publication Title: https://github.com/dair-ai/Prompt-Engineering-Guide original-date: 2022-12-16T16:04:50Z. ↗
461. [478] J. Savelka, K. D. Ashley, M. A. Gray, H. Westermann and H. Xu. 2023. Explaining legal concepts with augmented large language models (gpt-4). find
462. [479] T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni et al. 2022. Bloom: A 176b-parameter open-access multilingual language model. find
463. [480] R. Schaeffer, B. Miranda and S. Koyejo. 2023. Are emergent abilities of large language models a mirage? find our card
464. [481] T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda and T. Scialom. 2023. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761. find our card
465. [482] T. Schick, J. Dwivedi-Yu, Z. Jiang, F. Petroni, P. Lewis, G. Izacard, Q. You, C. Nalmpantis et al. 2022. Peer: A collaborative language model. find
466. [483] T. Schick and H. Schütze. 2021. It’s not just size that matters: Small language models are also few-shot learners. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2339– 2352. find our card
467. [484] J. Schulman, F. Wolski, P. Dhariwal, A. Radford and O. Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. find
468. [485] M. Schuster and K. Nakajima. 2012. Japanese and korean voice search. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5149–5152. find
469. [486] T. Schuster, R. Schuster, D. J. Shah and R. Barzilay. 2020. The limitations of stylometry for detecting machine-generated fake news. Computational Linguistics, 46(2):499–510. find
470. [487] R. Schwartz, J. Dodge, N. A. Smith and O. Etzioni. 2019. Green AI. ArXiv:1907.10597 [cs, stat]. find our card
471. [488] S. H. Schwartz, B. Breyer and D. Danner. 2015. Human values scale (ess). Zusammenstellung sozialwis-senschaftlicher Items und Skalen (ZIS). find
472. [489] A. See, A. Pappu, R. Saxena, A. Yerukola and C. D. Manning. 2019. Do massively pretrained language models make better storytellers? In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 843–861, Hong Kong, China. Association for Computational Linguisti… find
473. [490] R. Sennrich, B. Haddow and A. Birch. 2015. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909. find
474. [491] E. Sezgin, J. Sirrianni, S. L. Linwood et al. 2022. Operationalizing and implementing pretrained, large artificial intelligence linguistic models in the us health care system: Outlook of generative pretrained transformer 3 (gpt-3) as a service model. JMIR Medical Informatics, 10(2):e32875. find
475. [492] P. Shaw, J. Uszkoreit and A. Vaswani. 2018. Self-attention with relative position representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 464–468, New Orle… find our card
476. [493] N. Shazeer. 2019. Fast transformer decoding: One write-head is all you need. find
477. [494] N. Shazeer. 2019. Fast transformer decoding: One write-head is all you need. find
478. [495] N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton and J. Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538. find
479. [496] Z. Shen, M. Zhang, H. Zhao, S. Yi and H. Li. 2021. Efficient attention: Attention with linear complexities. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 3531–3539. find
480. [497] Y. Sheng, L. Zheng, B. Yuan, Z. Li, M. Ryabinin, B. Chen, P. Liang, C. Ré et al. 2023. High-throughput generative inference of large language models with a single gpu. find our card
481. [498] T. Shevlane, S. Farquhar, B. Garfinkel, M. Phuong, J. Whittlestone, J. Leung, D. Kokotajlo, N. Marchal et al. 2023. Model evaluation for extreme risks. arXiv preprint arXiv:2305.15324. find
482. [499] A. Shirafuji, Y. Watanobe, T. Ito, M. Morishita, Y. Nakamura, Y. Oda and J. Suzuki. 2023. Exploring the robustness of large language models for solving programming problems. find
483. [500] O. Shliazhko, A. Fenogenova, M. Tikhonova, V. Mikhailov, A. Kozlova and T. Shavrina. 2022. mgpt: Few-shot learners go multilingual. arXiv preprint arXiv:2204.07580. find our card
484. [501] M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper and B. Catanzaro. 2019. Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053. find
485. [502] K. Shridhar, J. Macina, M. El-Assady, T. Sinha, M. Kapur and M. Sachan. 2022. Automatic generation of socratic subquestions for teaching math word problems. ArXiv, abs/2211.12835. find
486. [503] K. Shridhar, A. Stolfo and M. Sachan. 2022. Distilling multi-step reasoning capabilities of large language models into smaller models via semantic decompositions. arXiv preprint arXiv:2212.00193. find our card
487. [504] D. Shrivastava, H. Larochelle and D. Tarlow. 2022. Repository-level prompt generation for large language models of code. arXiv preprint arXiv:2206.12839. — [arXiv:2206.12839](https://arxiv.org/abs/2206.12839) find
488. [505] R. W. Shuai, J. A. Ruffolo and J. J. Gray. 2021. Generative language modeling for antibody design. bioRxiv, pages 2021–12. find
489. [506] I. Shumailov, Z. Shumaylov, Y. Zhao, Y. Gal, N. Papernot and R. Anderson. 2023. The curse of recursion: Training on generated data makes models forget. find our card
490. [507] K. Shuster, S. Poff, M. Chen, D. Kiela and J. Weston. 2021. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567. find our card
491. [508] K. Shuster, J. Xu, M. Komeili, D. Ju, E. M. Smith, S. Roller, M. Ung, M. Chen et al. 2022. Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage. find our card
492. [509] S. Sia and K. Duh. 2023. In-context learning as maintaining coherency: A study of on-the-fly machine translation using large language models. ArXiv, abs/2305.03573. find
493. [510] I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason et al. 2022. Progprompt: Generating situated robot task plans using large language models. find our card
494. [511] K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani et al. 2022. Large language models encode clinical knowledge. find our card
495. [512] K. Singhal, T. Tu, J. Gottweis, R. Sayres, E. Wulczyn, L. Hou, K. Clark, S. Pfohl et al. 2023. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617. find our card
496. [513] A. Sinitsin, D. Pyrkin, A. Babenko, V. Plokhotnyuk and S. Popov. 2020. EDITABLE NEURAL NETWORKS. find
497. [514] S. L. Smith, P.-J. Kindermans, C. Ying and Q. V. Le. 2017. Don’t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489. find
498. [515] S. Smith, M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye et al. 2022. Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv preprint arXiv:2201.11990. find our card
499. [516] I. Solaiman and C. Dennison. 2021. Process for adapting language models to society (palms) with values-targeted datasets. Advances in Neural Information Processing Systems, 34:5861–5873. find
500. [517] S. Soltan, S. Ananthakrishnan, J. FitzGerald, R. Gupta, W. Hamza, H. Khan, C. Peris, S. Rawls et al. 2022. Alexatm 20b: Few-shot learning using a large-scale multilingual seq2seq model. arXiv preprint arXiv:2208.01448. find
501. [518] B. Sorscher, R. Geirhos, S. Shekhar, S. Ganguli and A. S. Morcos. 2022. Beyond neural scaling laws: beating power law scaling via data pruning. arXiv preprint arXiv:2206.14486. find our card
502. [519] A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro et al. 2022. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615. find our card
503. [529] X. Sun, T. Ge, F. Wei and H. Wang. 2021. Instantaneous grammatical error correction with shallow aggressive decoding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1:… find
504. [530] Y. Sun, S. Wang, S. Feng, S. Ding, C. Pang, J. Shang, J. Liu, X. Chen et al. 2021. Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. arXiv preprint arXiv:2107.02137. find our card
505. [531] Z. Sun. 2023. A short survey of viewing large language models in legal aspect. find
506. [532] D. Surís, S. Menon and C. Vondrick. 2023. Vipergpt: Visual inference via python execution for reasoning. arXiv preprint arXiv:2303.08128. find our card
507. [533] Susan Zhang [@suchenzang]. 2023. Piling on to the pile-on (sorry - it’s always easy to criticize), here’s a rant about benchmarks for LLMs that are used to back claims of "stronger" or "better" models. Let’s start with a tour through GPT-3’s Appendix G... 1/8. find
508. [520] J. Steinhardt. 2022. Future ml systems will be qualitatively different. Accessed May, 20:2022. find
509. [534] M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V. Le et al. 2022. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261. find our card
510. Emergent deception [521] J. Steinhardt. 2023. and emergent optimization. Available from: https://bounded-regret.ghost.io/ emergent-deception-optimization/. Accessed: 29/04/2023. ↗
511. [535] S. Swaminathan, A. Dedieu, R. V. Raju, M. Shanahan, M. Lazaro-Gredilla and D. George. 2023. Schema-learning and rebinding as mechanisms of in-context learning and emergence. ArXiv:2307.01201 [cs]. — [arXiv:2307.01201](https://arxiv.org/abs/2307.01201) find
512. [522] M. Stern, N. Shazeer and J. Uszkoreit. 2018. Block-wise parallel decoding for deep autoregressive models. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, page 10107–10116, Red Hook, NY, USA. Curran Associates Inc. find
513. [536] H. Tang, S. Gan, A. A. Awan, S. Rajbhandari, C. Li, X. Lian, J. Liu, C. Zhang et al. 2021. 1-bit adam: Communication efficient large-scale training with adam’s convergence speed. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learn… find
514. [523] C. Stevenson, I. Smal, M. Baas, R. Grasman and H. van der Maas. 2022. Putting gpt-3’s creativity to the (alternative uses) test. arXiv preprint arXiv:2206.08932. — [arXiv:2206.08932](https://arxiv.org/abs/2206.08932) find
515. [537] L. Tang, G. Uberti and T. Shlomi. 2023. Baselines for Identifying Watermarked Large Language Models. ArXiv:2305.18456 [cs]. — [arXiv:2305.18456](https://arxiv.org/abs/2305.18456) find
516. [524] N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei et al. 2020. Learning to summarize with human feedback. In Conference on Neural Information Processing Systems. find
517. [538] L. Tang, Z. Sun, B. Idnay, J. G. Nestor, A. Soroush, P. A. Elias, Z. Xu, Y. Ding et al. 2023. Evaluating large language models on medical evidence summarization. medRxiv, pages 2023–04. find
518. [525] A. Stolfo, Z. Jin, K. Shridhar, B. Schölkopf and M. Sachan. 2022. A causal framework to quantify the robustness of mathematical reasoning with language models. find
519. [539] R. Tang, Y.-N. Chuang and X. Hu. 2023. The Science of Detecting LLM-Generated Texts. ArXiv:2303.07205 [cs]. — [arXiv:2303.07205](https://arxiv.org/abs/2303.07205) find
520. [526] J. Su, Y. Lu, S. Pan, B. Wen and Y. Liu. 2021. Roformer: Enhanced transformer with rotary position embedding. find
521. [527] M. Sun, Z. Liu, A. Bair and J. Z. Kolter. 2023. A simple and effective pruning approach for large language models. find
522. [528] T. Sun, Y. Shao, H. Qian, X. Huang and X. Qiu. 2022. Black-box tuning for language-model-as-a-service. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 20841–20855. PMLR. find
523. [540] R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang and T. B. Hashimoto. 2023. Alpaca: A strong, replicable instruction-following model. find
524. [541] Y. Tay, D. Bahri, D. Metzler, D.-C. Juan, Z. Zhao and C. Zheng. 2021. Synthesizer: Rethinking self-attention for transformer models. In International conference on machine learning, pages 10183–10192. PMLR. find
525. [542] Y. Tay, M. Dehghani, S. Abnar, H. W. Chung, W. Fedus, J. Rao, S. Narang, V. Q. Tran et al. 2022. Scaling laws vs model architectures: How does inductive bias influence scaling? find
526. [543] Y. Tay, M. Dehghani, D. Bahri and D. Metzler. 2022. Efficient transformers: A survey. ACM Computing Surveys, 55(6):1–28. find our card
527. [544] Y. Tay, M. Dehghani, J. Rao, W. Fedus, S. Abnar, H. W. Chung, S. Narang, D. Yogatama et al. 2022. Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers. ArXiv:2109.10686 [cs]. — [arXiv:2109.10686](https://arxiv.org/abs/2109.10686) find
528. [545] Y. Tay, M. Dehghani, V. Q. Tran, X. Garcia, J. Wei, X. Wang, H. W. Chung, D. Bahri et al. 2022. Ul2: Unifying language learning paradigms. find our card
529. [546] Y. Tay, V. Q. Tran, S. Ruder, J. Gupta, H. W. Chung, D. Bahri, Z. Qin, S. Baumgartner et al. 2022. Charformer: Fast character transformers via gradient-based subword tokenization. find
530. [547] Y. Tay, J. Wei, H. W. Chung, V. Q. Tran, D. R. So, S. Shakeri, X. Garcia, H. S. Zheng et al. 2022. Transcending scaling laws with 0.1% extra compute. find
531. [548] R. Taylor, M. Kardas, G. Cucurull, T. Scialom, A. Hartshorn, E. Saravia, A. Poulton, V. Kerkez et al. 2022. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085. find our card
532. [549] W. L. Taylor. 1953. “cloze procedure”: A new tool for measuring readability. Journalism quarterly, 30(4):415– 433. find
533. [550] J. Thiergart, S. Huber and T. Übellacker. 2021. Understanding emails and drafting responses–an approach using gpt-3. arXiv preprint arXiv:2102.03062. — [arXiv:2102.03062](https://arxiv.org/abs/2102.03062) find
534. [551] R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos et al. 2022. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239. find our card
535. [552] R. Tian, S. Narayan, T. Sellam and A. P. Parikh. 2020. Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation. ArXiv:1910.08684 [cs]. — [arXiv:1910.08684](https://arxiv.org/abs/1910.08684) find
536. [559] A. Uchendu, T. Le, K. Shu and D. Lee. 2020. Authorship Attribution for Neural Text Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8384–8395, Online. Association for Computational Linguistics. find
537. [560] J. Uesato, N. Kushman, R. Kumar, F. Song, N. Siegel, L. Wang, A. Creswell, G. Irving et al. 2022. Solving math word problems with process- and outcome-based feedback. find our card
538. [561] S. University. 2023. Holistic evaluation of langauge models results page. Available from: https://crfm. stanford.edu/helm/latest/?groups=1. Accessed: 23/03/2023. find
539. [562] K. Valmeekam, A. Olmo, S. Sreedharan and S. Kambhampati. 2023. Large language models still can’t plan (a benchmark for llms on planning and reasoning about change). find
540. [563] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser and I. Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. find
541. [564] S. Vemprala, R. Bonatti, A. Bucker and A. Kapoor. 2023. Chatgpt for robotics: Design principles and model abilities. find
542. [565] A. Venigalla, J. Frankle and M. Carbin. 2022. Pubmed gpt: A domain- specific large language model for biomedical text. https://www.mosaicml.com/blog/ introducing-pubmed-gpt. Accessed: 2023-01-24. ↗
543. [566] R. Verkuil, O. Kabeli, Y. Du, B. I. Wicky, L. F. Milles, J. Dauparas, D. Baker, S. Ovchinnikov et al. 2022. Language models generalize beyond natural proteins. bioRxiv, pages 2022–12. find
544. [567] A. Vijayakumar, M. Cogswell, R. Selvaraju, Q. Sun, S. Lee, D. Crandall and D. Batra. 2018. Diverse beam search for improved description of complex scenes. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). find
545. [553] K. Tirumala, A. H. Markosyan, L. Zettlemoyer and A. Aghajanyan. Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models. [568] P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn and A. Ho. 2022. Will we run out of data? an analysis of the limits of… find
546. [554] H. Q. To, N. D. Bui, J. Guo and T. N. Nguyen. 2023. Better language models of code through self-improvement. arXiv preprint arXiv:2304.01228. find
547. [569] H. Viswanath and T. Zhang. 2023. Fairpy: A toolkit for evaluation of social biases and their mitigation in large language models. arXiv preprint arXiv:2302.05508. — [arXiv:2302.05508](https://arxiv.org/abs/2302.05508) find
548. [555] A. Tornede, D. Deng, T. Eimer, J. Giovanelli, A. Mohan, T. Ruhkopf, S. Segel, D. Theodorakopoulos et al. 2023. AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. ArXiv:2306.08107 [cs]. find
549. [570] J. von Oswald, E. Niklasson, E. Randazzo, J. Sacramento, A. Mordvintsev, A. Zhmoginov and M. Vladymyrov. 2022. Transformers learn in-context by gradient descent. arXiv preprint arXiv:2212.07677. find our card
550. [556] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal et al. 2023. LLaMA: Open and Efficient Foundation Language Models. ArXiv:2302.13971 [cs]. find
551. [572] T. Vu, B. Lester, N. Constant, R. Al-Rfou’ and D. Cer. 2022. SPoT: Better frozen model adaptation through soft prompt transfer. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5039–5059, Dublin, Ireland. Association for… find our card
552. [557] H. Touvron, L. Martin and K. Stone. Llama 2: Open Foundation and Fine-Tuned Chat Models. [558] C. Tran, S. Khadkikar and A. Porollo. 2023. Survey of protein sequence embedding models. International Journal of Molecular Sciences, 24(4):3775. find
553. [571] H. d. Vries. 2023. Go smol or go home. find
554. [573] J. P. Wahle, T. Ruas, T. Foltỳnek, N. Meuschke and B. Gipp. 2022. Identifying machine-paraphrased plagiarism. In International Conference on Information, pages 393–413. Springer. find
555. [574] J. P. Wahle, T. Ruas, F. Kirstein and B. Gipp. 2022. How large language models are transforming machine-paraphrased plagiarism. arXiv preprint arXiv:2210.03568. — [arXiv:2210.03568](https://arxiv.org/abs/2210.03568) find
556. [588] Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi and H. Hajishirzi. 2022. Self-instruct: Aligning language model with self generated instructions. find
557. [575] A. Wang, A. Singh, J. Michael, F. Hill, O. Levy and S. Bowman. 2018. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 353–355, Brussels, Belgiu… find our card
558. [589] Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Naik, A. Ashok, A. S. Dhanasekaran et al. 2022. Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing… find
559. GPT-J-[576] B. Wang and A. Komatsuzaki. 2021. 6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. ↗
560. [577] C. Wang, K. Cho and J. Gu. 2020. Neural machine translation with byte-level subwords. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):9154– 9160. find
561. [578] C. Wang, X. Liu, Z. Chen, H. Hong, J. Tang and D. Song. 2022. DeepStruct: Pretraining of language models for structure prediction. In Findings of the Association for Computational Linguistics: ACL 2022, pages 803–823, Dublin, Ireland. Association for Computational Linguistics. find
562. [579] G. Wang, Y. Xie, Y. Jiang, A. Mandlekar, C. Xiao, Y. Zhu, L. Fan and A. Anandkumar. 2023. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291. find
563. [580] H. Wang, J. Kaddour, S. Liu, J. Tang, M. Kusner, J. Lasenby and Q. Liu. 2022. Evaluating self-supervised learning for molecular graph embeddings. arXiv preprint arXiv:2206.08005. — [arXiv:2206.08005](https://arxiv.org/abs/2206.08005) find
564. [581] P. Wang, L. Li, L. Chen, D. Zhu, B. Lin, Y. Cao, Q. Liu, T. Liu et al. 2023. Large Language Models are not Fair Evaluators. ArXiv:2305.17926 [cs]. find
565. [582] R. Wang, H. Wang, F. Mi, Y. Chen, R. Xu and K.-F. Wong. 2023. Self-critique prompting with large language models for inductive instructions. arXiv preprint arXiv:2305.13733. — [arXiv:2305.13733](https://arxiv.org/abs/2305.13733) find
566. [590] Y. Wang, Y. Zhao and L. Petzold. 2023. Are large language models ready for healthcare? a comparative study on clinical language understanding. find
567. [591] Z. Wang, S. Cai, A. Liu, X. Ma and Y. Liang. 2023. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560. find our card
568. [592] Z. Wang, J. Wohlwend and T. Lei. 2019. Structured pruning of large language models. arXiv preprint arXiv:1910.04732. find
569. [593] Z. Wang, Z. Dai, B. Póczos and J. Carbonell. 2019. Characterizing and avoiding negative transfer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11293–11302. find
570. [594] Z. Wang, M. Zoghi, F. Hutter, D. Matheson, N. De Freitas et al. 2013. Bayesian optimization in high dimensions via random embeddings. In IJCAI, volume 13, pages 1778– 1784. find
571. [595] T. Webb, K. J. Holyoak and H. Lu. 2022. Emergent analogical reasoning in large language models. find
572. [596] A. Webson and E. Pavlick. 2022. Do prompt-based models really understand the meaning of their prompts? In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2300–2344, Seattle, United States. Ass… find our card
573. [597] A. Wei, N. Haghtalab and J. Steinhardt. 2023. Jailbroken: How Does LLM Safety Training Fail? ArXiv:2307.02483 [cs]. find our card
574. [583] S. Wang, Y. Liu, Y. Xu, C. Zhu and M. Zeng. 2021. Want to reduce labeling cost? gpt-3 can help. find our card
575. [598] J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai et al. 2022. Finetuned language models are zero-shot learners. In International Conference on Learning Representations. find
576. [584] S. Wang, S. Menon, T. Long, K. Henderson, D. Li, K. Crowston, M. Hansen, J. V. Nickerson et al. 2023. Reelframer: Co-creating news reels on social media with generative ai. arXiv preprint arXiv:2304.09653. — [arXiv:2304.09653](https://arxiv.org/abs/2304.09653) find
577. [599] J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma et al. 2022. Emergent abilities of large language models. find our card
578. [585] X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery and D. Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. find our card
579. [586] Y. Wang, Z. Yu, Z. Zeng, L. Yang, C. Wang, H. Chen, C. Jiang, R. Xie et al. 2023. Pandalm: An automatic evaluation benchmark for llm instruction tuning optimization. arXiv preprint arXiv:2306.05087. find our card
580. [587] Y. Wang. 2021. Comment section personalization: Algorithmic, interface, and interaction design. In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pages 84–88, Online. Association for Computational Linguistics. find
581. [600] J. Wei, Y. Tay and Q. V. Le. 2022. Inverse scaling can become u-shaped. arXiv preprint arXiv:2211.02011. find
582. [601] J. Wei, X. Wang, D. Schuurmans, M. Bosma, brian ichter, F. Xia, E. H. Chi, Q. V. Le et al. 2022. Chain of thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems. find our card
583. [602] L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese et al. 2021. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359. find our card
584. [603] M. Weiss. 2019. Deepfake bot submissions to federal public comment websites cannot be distinguished from human submissions. Technology Science, 2019121801. find
585. [604] S. Welleck, I. Kulikov, S. Roller, E. Dinan, K. Cho and J. Weston. 2019. Neural text generation with unlikelihood training. arXiv preprint arXiv:1908.04319. find our card
586. [621] Q. Xie, Z. Luo, B. Wang and S. Ananiadou. 2023. A survey on biomedical text summarization with pre-trained language model. find
587. [605] L. Weng. 2023. Large transformer model inference optimization. Lil’Log. find
588. [622] S. M. Xie, H. Pham, X. Dong, N. Du, H. Liu, Y. Lu, P. Liang, Q. V. Le et al. 2023. DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. ArXiv:2305.10429 [cs]. find
589. [606] L. Weng. 2023. weng.github.io. find
590. Prompt engineering. lilian-[607] M. Willig, M. ZEČEVIĆ, D. S. Dhami and K. Kersting. 2023. Causal parrots: Large language models may talk causality but are not causal. preprint. find
591. [608] F. Winkelmolen, N. Ivkin, H. F. Bozkurt and Z. Karnin. 2020. Practical and sample efficient zero-shot hpo. arXiv preprint arXiv:2007.13382. — [arXiv:2007.13382](https://arxiv.org/abs/2007.13382) find
592. [609] Y. Wolf, N. Wies, Y. Levine and A. Shashua. 2023. Fundamental limitations of alignment in large language models. arXiv preprint arXiv:2304.11082. find
593. [610] M. Wornow, Y. Xu, R. Thapa, B. Patel, E. Steinberg, S. Fleming, M. A. Pfeffer, J. Fries et al. 2023. The shaky foundations of clinical foundation models: A survey of large language models and foundation models for emrs. find
594. [611] F. Wu, D. Radev and J. Xu. 2023. When geometric deep learning meets pretrained protein language models. bioRxiv, pages 2023–01. find
595. [612] J. Wu, L. Ouyang, D. M. Ziegler, N. Stiennon, R. Lowe, J. Leike and P. Christiano. 2021. Recursively summarizing books with human feedback. arXiv preprint arXiv:2109.10862. find our card
596. [613] J. Wu, F. Wu, B. Jiang, W. Liu and P. Zhao. 2022. tfold-ab: Fast and accurate antibody structure prediction without sequence homologs. bioRxiv, pages 2022–11. find
597. [614] P. Y. Wu, J. A. Tucker, J. Nagler and S. Messing. 2023. Large language models can be used to estimate the ideologies of politicians in a zero-shot learning setting. find
598. [615] S. Wu, X. Zhao, T. Yu, R. Zhang, C. Shen, H. Liu, F. Li, H. Zhu et al. 2021. Yuan 1.0: Large-scale pre-trained language model in zero-shot and few-shot learning. find
599. [616] S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg et al. 2023. Bloomberggpt: A large language model for finance. find our card
600. [617] Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. find
601. [618] Y. Wu, M. Gardner, P. Stenetorp and P. Dasigi. 2022. Generating data to mitigate spurious correlations in natural language inference datasets. arXiv preprint arXiv:2203.12942. find
602. [619] Z. Wu, L. Qiu, A. Ross, E. Akyürek, B. Chen, B. Wang, N. Kim, J. Andreas et al. 2023. Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks. ArXiv:2307.02477 [cs]. find
603. [620] Y. Xiao and W. Y. Wang. 2021. On Hallucination and Predictive Uncertainty in Conditional Language Generation. ArXiv:2103.15025 [cs]. find
604. [623] S. M. Xie, A. Raghunathan, P. Liang and T. Ma. 2022. An Explanation of In-context Learning as Implicit Bayesian Inference. ArXiv:2111.02080 [cs]. find our card
605. [624] S. M. Xie, S. Santurkar, T. Ma and P. Liang. 2023. Data Selection for Language Models via Importance Resampling. ArXiv:2302.03169 [cs]. find
606. [625] C. Xu, Q. Sun, K. Zheng, X. Geng, P. Zhao, J. Feng, C. Tao and D. Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. arXiv preprint arXiv:2304.12244. find our card
607. [626] F. F. Xu, U. Alon, G. Neubig and V. J. Hellendoorn. 2022. A systematic evaluation of large language models of code. find
608. [627] M. Xu, X. Yuan, S. Miret and J. Tang. 2023. Protst: Multi-modality learning of protein sequences and biomedical texts. arXiv preprint arXiv:2301.12040. — [arXiv:2301.12040](https://arxiv.org/abs/2301.12040) find
609. [628] Y. Xu, H. Lee, D. Chen, B. Hechtman, Y. Huang, R. Joshi, M. Krikun, D. Lepikhin et al. 2021. Gspmd: general and scalable parallelization for ml computation graphs. arXiv preprint arXiv:2105.04663. — [arXiv:2105.04663](https://arxiv.org/abs/2105.04663) find
610. [629] L. Xue, A. Barua, N. Constant, R. Al-Rfou, S. Narang, M. Kale, A. Roberts and C. Raffel. 2022. ByT5: Towards a token-free future with pre-trained byte-to-byte models. ArXiv:2105.13626 [cs]. find
611. [630] L. Xue, A. Barua, N. Constant, R. Al-Rfou, S. Narang, M. Kale, A. Roberts and C. Raffel. 2022. ByT5: Towards a token-free future with pre-trained byte-to-byte models. Transactions of the Association for Computational Linguistics, 10:291–306. find
612. [631] L. Xue, N. Constant, A. Roberts, M. Kale, R. Al-Rfou, A. Siddhant, A. Barua and C. Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langu… find
613. [632] L. Yan, L. Sha, L. Zhao, Y. Li, R. Martinez-Maldonado, G. Chen, X. Li, Y. Jin et al. 2023. Practical and ethical challenges of large language models in education: A systematic literature review. find
614. [633] G. Yang, E. Hu, I. Babuschkin, S. Sidor, X. Liu, D. Farhi, N. Ryder, J. Pachocki et al. 2021. Tuning large neural networks via zero-shot hyperparameter transfer. Advances in Neural Information Processing Systems, 34:17084–17097. find
615. [634] J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, B. Yin and X. Hu. 2023. Harnessing the power of llms in practice: A survey on chatgpt and beyond. find our card
616. [635] K. Yang and D. Klein. 2021. Fudge: Controlled text generation with future discriminators. arXiv preprint arXiv:2104.05218. find our card
617. [636] K. Yang, D. Klein, N. Peng and Y. Tian. 2022. Doc: Improving long story coherence with detailed outline control. arXiv preprint arXiv:2212.10077. find
618. [637] K. Yang, N. Peng, Y. Tian and D. Klein. 2022. Re3: Generating longer stories with recursive reprompting and revision. arXiv preprint arXiv:2210.06774. find
619. [638] X. Yang, K. Chen, W. Zhang, C. Liu, Y. Qi, J. Zhang, H. Fang and N. Yu. 2023. Watermarking Text Generated by Black-Box Language Models. ArXiv:2305.08883 [cs]. — [arXiv:2305.08883](https://arxiv.org/abs/2305.08883) find
620. [639] S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao and K. Narasimhan. 2023. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. ArXiv:2305.10601 [cs]. find
621. [650] R. You, Y. Liu, H. Mamitsuka and S. Zhu. 2021. Bertmesh: deep contextual representation learning for large-scale high-performance mesh indexing with full text. Bioinformatics, 37(5):684–692. find
622. [651] F. Yu, L. Quartey and F. Schilder. 2022. Legal prompting: Teaching a language model to think like a lawyer. arXiv preprint arXiv:2212.01326. find
623. [652] L. Yu, D. Simig, C. Flaherty, A. Aghajanyan, L. Zettlemoyer and M. Lewis. 2023. Megabyte: Predicting million-byte sequences with multiscale transformers. arXiv preprint arXiv:2305.07185. find
624. [653] P. Yu, M. Artetxe, M. Ott, S. Shleifer, H. Gong, V. Stoyanov and X. Li. 2022. Efficient language modeling with sparse all-mlp. find
625. [640] S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan and Y. Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629. find our card
626. [654] P. Yu, T. Wang, O. Golovneva, B. Alkhamissy, G. Ghosh, M. Diab and A. Celikyilmaz. 2022. Alert: Adapting language models to reasoning tasks. arXiv preprint arXiv:2212.08286. — [arXiv:2212.08286](https://arxiv.org/abs/2212.08286) find
627. [641] X. Yao, Y. Zheng, X. Yang and Z. Yang. 2022. NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework. In Proceedings of the 39th International Conference on Machine Learning, pages 25438– 25451. PMLR. ISSN: 2640-3498. find
628. [655] L. Yunxiang, L. Zihan, Z. Kai, D. Ruilong and Z. You. 2023. Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge. find
629. [642] Y. Yao, P. Wang, B. Tian, S. Cheng, Z. Li, S. Deng, H. Chen and N. Zhang. 2023. Editing Large Language Models: Problems, Methods, and Opportunities. ArXiv:2305.13172 [cs]. find
630. [643] Z. Yao, R. Y. Aminabadi, M. Zhang, X. Wu, C. Li and Y. He. 2022. Zeroquant: Efficient and affordable post-training quantization for large-scale transformers. arXiv preprint arXiv:2206.01861. find our card
631. [644] M. Yasunaga, A. Bosselut, H. Ren, X. Zhang, C. D. Manning, P. Liang and J. Leskovec. 2022. Deep bidirectional language-knowledge graph pretraining. arXiv preprint arXiv:2210.09338. — [arXiv:2210.09338](https://arxiv.org/abs/2210.09338) find
632. [656] E. Zelikman, Y. Wu, J. Mu and N. Goodman. 2022. STar: Bootstrapping reasoning with reasoning. In Advances in Neural Information Processing Systems. find our card
633. [657] R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roesner and Y. Choi. 2019. Defending against neural fake news. Advances in neural information processing systems, 32. find our card
634. [658] A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu et al. 2022. Glm-130b: An open bilingual pre-trained model. find our card
635. [659] W. Zeng, X. Ren, T. Su, H. Wang, Y. Liao, Z. Wang, X. Jiang, Z. Yang et al. 2021. Pangu-α: Large-scale autoregressive pretrained chinese language models with auto-parallel computation. find
636. [645] S. Yi, R. Goel, C. Khatri, A. Cervone, T. Chung, B. Hedayatnia, A. Venkatesh, R. Gabriel et al. 2019. Towards coherent and engaging spoken dialog response generation using automatic conversation evaluators. In Proceedings of the 12th International Conference on Natural Language Generation, pag… find
637. [660] F. Zhang, B. Chen, Y. Zhang, J. Liu, D. Zan, Y. Mao, J.-G. Lou and W. Chen. 2023. Repocoder: Repository-level code completion through iterative retrieval and generation. find
638. [646] D. Yogatama, C. de Masson d’Autume and L. Kong. 2021. Adaptive semiparametric language models. Transactions of the Association for Computational Linguistics, 9:362–373. find
639. [662] H. Zhang, D. Duckworth, D. Ippolito and A. Neelakantan. 2021. Trading off diversity and quality in natural language generation. In Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval), pages 25– 33, Online. Association for Computational Linguistics. find
640. [647] T. Yoneda, J. Fang, P. Li, H. Zhang, T. Jiang, S. Lin, B. Picker, D. Yunis et al. 2023. Statler: State-maintaining language models for embodied reasoning. find
641. [648] K. M. Yoo, D. Park, J. Kang, S.-W. Lee and W. Park. 2021. GPT3Mix: Leveraging large-scale language models for text augmentation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2225– 2239, Punta Cana, Dominican Republic. Association for Computational Linguistics… find our card
642. [649] K. Yoo, W. Ahn, J. Jang and N. Kwak. 2023. Robust Natural Language Watermarking through Invariant Features. ArXiv:2305.01904 [cs]. — [arXiv:2305.01904](https://arxiv.org/abs/2305.01904) find
643. [661] H. Zhang, L. H. Li, T. Meng, K.-W. Chang and G. V. d. Broeck. 2022. On the Paradox of Learning to Reason from Data. ArXiv:2205.11502 [cs]. — [arXiv:2205.11502](https://arxiv.org/abs/2205.11502) find
644. [663] M. Zhang and Y. He. 2020. Accelerating training of transformer-based language models with progressive layer dropping. find
645. [664] M. Zhang, O. Press, W. Merrill, A. Liu and N. A. Smith. 2023. How Language Model Hallucinations Can Snowball. ArXiv:2305.13534 [cs]. find our card
646. [665] S. Zhang. 2023. [...] that’s an unhelpful order of magnitude difference in how large of a model you should be training in order to be considered “compute optimal”. https://twitter.com/suchenzang/ status/1616752494608007171?s=20. Accessed: 2023-06-06. ↗
647. [666] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab et al. 2022. Opt: Open pre-trained transformer language models. find
648. [681] C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat et al. 2023. LIMA: Less Is More for Alignment. ArXiv:2305.11206 [cs]. find
649. [667] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger and Y. Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675. find
650. [682] D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui et al. 2022. Least-to-most prompting enables complex reasoning in large language models. find our card
651. [668] T. Zhang, F. Ladhak, E. Durmus, P. Liang, K. McKeown and T. B. Hashimoto. 2023. Benchmarking large language models for news summarization. find our card
652. [683] Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan and J. Ba. 2023. Large language models are human-level prompt engineers. In International Conference on Learning Representations. find our card
653. [669] Z. Zhang, Y. Gu, X. Han, S. Chen, C. Xiao, Z. Sun, Y. Yao, F. Qi et al. 2021. Cpm-2: Large-scale cost-effective pre-trained language models. find
654. [670] Z. Zhang, Y. Lin, Z. Liu, P. Li, M. Sun and J. Zhou. 2022. Moefication: Transformer feed-forward layers are mixtures of experts. find
655. [671] Z. Zhang, A. Zhang, M. Li and A. Smola. 2022. Automatic chain of thought prompting in large language models. find our card
656. [672] S. Zhao, J. Wen, L. A. Tuan, J. Zhao and J. Fu. 2023. Prompt as triggers for backdoor attack: Examining the vulnerability in language models. arXiv preprint arXiv:2305.01219. find
657. [673] W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang et al. 2023. A Survey of Large Language Models. ArXiv:2303.18223 [cs]. find our card
658. [674] Y. Zhao, A. Gu, R. Varma, L. Luo, C.-C. Huang, M. Xu, L. Wright, H. Shojanazeri et al. 2023. Pytorch fsdp: experiences on scaling fully sharded data parallel. arXiv preprint arXiv:2304.11277. find
659. [684] Y. Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba and S. Fidler. 2015. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. find our card
660. [685] B. Zhuang, J. Liu, Z. Pan, H. He, Y. Weng and C. Shen. 2023. A survey on efficient training of transformers. arXiv preprint arXiv:2302.01107. — [arXiv:2302.01107](https://arxiv.org/abs/2302.01107) find
661. [686] D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano and G. Irving. 2019. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593. find
662. [687] B. Zoph, I. Bello, S. Kumar, N. Du, Y. Huang, J. Dean, N. Shazeer and W. Fedus. 2022. St-moe: Designing stable and transferable sparse expert models. find
663. [688] M. Zvyagin, A. Brace, K. Hippe, Y. Deng, B. Zhang, C. O. Bohorquez, A. Clyde, B. Kale et al. 2022. Genslms: Genome-scale language models reveal sars-cov-2 evolutionary dynamics. bioRxiv, pages 2022–10. find
664. [675] Z. Zhao, E. Wallace, S. Feng, D. Klein and S. Singh. 2021. Calibrate before use: Improving few-shot performance of language models. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 12697–12706. PMLR. find our card
665. [676] B. Zheng, L. Dong, S. Huang, S. Singhal, W. Che, T. Liu, X. Song and F. Wei. 2021. Allocating large vocabulary capacity for cross-lingual language model pre-training. arXiv preprint arXiv:2109.07306. find
666. [677] L. Zheng, Z. Li, H. Zhang, Y. Zhuang, Z. Chen, Y. Huang, Y. Wang, Y. Xu et al. 2022. Alpa: Automating inter- and Intra-Operator parallelism for distributed deep learning. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22), pages 559–578, Carlsbad, CA. USENIX Asso… find
667. [678] R. Zheng, S. Dou, S. Gao, W. Shen, B. Wang, Y. Liu, S. Jin, Q. Liu et al. 2023. Secrets of RLHF in Large Language Models Part I: PPO. ArXiv:2307.04964 [cs]. find our card
668. [679] W. Zhong, R. Cui, Y. Guo, Y. Liang, S. Lu, Y. Wang, A. Saied, W. Chen et al. 2023. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv:2304.06364. find our card
669. [680] A. Zhou, Y. Ma, J. Zhu, J. Liu, Z. Zhang, K. Yuan, W. Sun and H. Li. 2021. Learning N: M fine-grained structured sparse neural networks from scratch. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. find