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Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide

Paper · 2026 · Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe et al.

Summary

This TACL survey examines how to adapt large language models when labeled data is scarce. That situation is common for low-resource languages, specialized fields such as medicine or law, and deployments with limited compute. The authors introduce no new dataset or model. Instead, they organize recent literature across the whole post-pretraining pipeline in four parts:

  • Parameter-efficient fine-tuning (PEFT): selective, low-rank, prompt-based, adapter and hybrid methods.
  • Domain and cross-lingual adaptation: for both encoder and decoder architectures.
  • Specialization techniques: contrastive, semi-supervised and active learning.
  • Preference alignment: using small amounts of human or synthetic feedback.

For each family, the survey summarizes empirical trade-offs, data requirements and selection guidance. Recurring conclusions:

  • Bigger models learn more from fewer examples.
  • Careful data curation beats sheer volume.
  • Encoders remain strong for discriminative tasks.
  • Model merging is a promising but poorly understood tool.

The paper matters as a practical decision guide for teams working on underrepresented languages, Ukrainian among them. It also calls for more transparent reporting and better non-English benchmarks.

Key points

  • Full fine-tuning pays off only with roughly million-sample datasets. Below about 100k samples, PEFT typically equals or beats it, while selective and LoRA-style methods update under 1% of weights.
  • Raising LoRA rank (4 → 128) or prompt length (50 → 600) yields only small gains up to 100k samples. Under a fixed budget, the authors recommend a larger model with 8- or 4-bit quantization instead.
  • Inference cost differs by method. Sequential adapters can slow inference by up to ~40% and soft prompts by 30–40%, whereas LoRA can be merged into the weights with no added latency.
  • Encoders are sample-efficient: RoBERTa (335M) comes near T5 (11B) on discriminative tasks.
  • For cross-lingual work, translating training data generally beats direct transfer or translating test data. Multilingual SFT prompts improve generalization. Model merging helps low-resource languages, but its benefits fade below ~2B parameters.

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

 Institutions

🎓Imperial College LondonLondon · United Kingdom50 works · 13,154 citations🎓Technical University of MunichMunich · Germany62 works · 48,970 citations🎓Munich Center for Machine LearningMunich · Germany62 works · 1,167 citations🏥TUM KlinikumMunich · Germany3 works · 234 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe and Florian Hinterwimmer. 2026. Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.627

@article{szep2026fine,
  title = {{Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide}},
  author = {Marton Szep, Daniel Rueckert, Rüdiger {von Eisenhart-Rothe} and Florian Hinterwimmer},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.627},
  url = {https://doi.org/10.1162/tacl.a.627}
}

References

229 references, 82 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Argilla and MantisNLP. 2024. RLHF and Alternatives: Overview. find
  2. Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler. 2021. ExT5: Towards extreme multi-task scaling for transfer learning. In International Conferen…
  3. Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer. 2021. Intrinsic dimensionality explains the effectiveness of language model fine-tuning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Pro…
  4. Jordi Armengol-Estapé, Ona de Gibert Bonet, and Maite Melero. 2022. On the multilingual capabilities of very large-scale english language models. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3056–3068, Marseille, France. European Language Resources Associatio… find
  5. Ahtamjan Ahmat, Yating Yang, Bo Ma, Rui Dong, Kaiwen Lu, and Lei Wang. 2023. WAD-X: Improving zero-shot cross-lingual transfer via adapter-based word alignment. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(9):224:1–224:23. https://doi.org/10.1145/3610289 — [doi:10.1145/3610289](https://doi.org/10.1145/3610289) ↗
  6. Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. 2020. On the cross-lingual transferability of monolingual representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4623–4637, Online. Association for Computational Linguistics. https://doi .o…
  7. Jesujoba Alabi, Kwabena Amponsah-Kaakyire, David Adelani, and Cristina España-Bonet. 2020. Massive vs. curated embeddings for low-resourced languages: The case of Yorùbá and Twi. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2754–2762, Marseille, France. Europe… find
  8. Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello. 2024. A general theoretical paradigm to understand Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, and Dietrich Klakow. 2022. learning from human preferences. In Pro… find
  9. Fan Bai, Alan Ritter, and Wei Xu. 2021. Pre-train or annotate? Domain adaptation with a constrained budget. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5002–5015, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics… find
  10. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Sh… find
  11. Grover. 2025. Comparing bad apples to good oranges: Aligning large language models via joint preference optimization. ArXiv:2404 .00530v2 [cs]. https://doi.org/10.18653/v1/2025.findings-acl.39 ↗
  12. Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, and Andrew McCallum. 2020. Self-supervised meta-learning for few-shot natural language classification tasks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 522–534, Online. Association for… find
  13. Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. 2022. Efficient training of language models to fill in the middle. ArXiv:2207.14255v1 [cs].
  14. Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. 2022. BitFit: 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. Asso…
  15. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared… — Constitutional AI: Harmlessness from AI Feedback
  16. Alberto Benayas, Miguel Angel Sicilia, and Marçal Mora-Cantallops. 2024. A comparative analysis of encoder-only and decoder-only models in intent classification and sentiment analysis: Navigating the trade-offs in model size and performance. Language Resources and Evaluation. https://doi.org /10.21… — [doi:10.21203/rs.3.rs-3865391/v1](https://doi.org/10.21203/rs.3.rs-3865391/v1) ↗
  17. Terra Blevins and Luke Zettlemoyer. 2022. Language contamination helps explains the cross-lingual capabilities of English pretrained models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3563–3574, Abu Dhabi, United Arab Emirates. Association for Co… find
  18. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Hritik Bansal, Ashima Suvarna, Gantavya Bhatt, Nanyun Peng, Kai-Wei Chang, and Aditya Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.194 ↗
  19. Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin C… — Language Models are Few-Shot Learners
  20. Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, and Diyi Yang. 2022a. Parameter-efficient fine-tuning design spaces. In International Conference on Learning Representations. https://doi.org/10.1093/bioinformatics/btad496 ↗
  21. Peng Chen, Jian Wang, Hongfei Lin, Di Zhao, and Zhihao Yang. 2023c. Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning. Bioinformatics, 39(8):btad496. find
  22. Tommaso Mario Buonocore, Claudio Crema, Alberto Redolfi, Riccardo Bellazzi, and Enea Parimbelli. 2023. Localizing in-domain adaptation of transformer-based biomedical language models. Journal of Biomedical Informatics, 144:104431. https://doi.org/10.1016/j.jbi.2023.104431, PubMed: 37385327 ↗
  23. Yuxuan Chen, David Harbecke, and Leonhard Hennig. 2022b. Multilingual relation classification via efficient and effective prompting. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1059–1075, Abu Dhabi, United Arab Emirates. Association for Computatio… find
  24. Steven Cao, Nikita Kitaev, and Dan Klein. 2019. Multilingual alignment of contextual word representations. In International Conference on Learning Representations. find
  25. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. 2024b. Self-play fine-tuning converts weak language models to strong language models. In Proceedings of the 41st International Conference on Machine Learning, pages 6621–6642. PMLR. find
  26. Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, and Kyunghyun Cho. 2024a. Preference learning algorithms do not learn preference rankings. Advances in Neural Information Processing Systems, 37:101928–101968. https://doi.org/10.52202/079017-3234 ↗
  27. Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, and Ming Zhou. 2021a. InfoXLM: An information-theoretic framework for cross-lingual language model pre-training. In Proceedings of the 2021 Conference of the North American Chapter of the Asso…
  28. Hao Chen, Yiming Zhang, Qi Zhang, Hantao Yang, Xiaomeng Hu, Xuetao Ma, Yifan Yanggong, and Junbo Zhao. 2023a. Maybe only 0.5% data is needed: A preliminary exploration of low training data instruction tuning. ArXiv:2305.09246v1 [cs]. — [arXiv:2305.09246](https://arxiv.org/abs/2305.09246) find
  29. Jiaao Chen, Derek Tam, Colin Raffel, Mohit Bansal, and Diyi Yang. 2023b. An empirical survey of data augmentation for limited data learning in NLP. Transactions of the Association for Computational Linguistics, 11:191–211. https://doi.org/10.1162/tacl a 00542 ↗
  30. Zewen Chi, Li Dong, Bo Zheng, Shaohan Huang, Xian-Ling Mao, Heyan Huang, and Furu Wei. 2021b. Improving pretrained cross-lingual language models via self-labeled word alignment. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Join… find
  31. Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.265 ↗
  32. Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc. find
  33. Jen-Tzung Chien, Ming-Yen Chen, and Jing-Hao Xue. 2023. Learning meta soft prompt for few-shot language models. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pages 57–62. https://doi.org/10.1109/APSIPAASC58517.2023 .10317500 — [doi:10.1109/apsipaasc58517.2023](https://doi.org/10.1109/apsipaasc58517.2023) ↗
  34. Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. find
  35. Claudio Crema, Tommaso Mario Buonocore, Silvia Fostinelli, Enea Parimbelli, Federico Verde, Cira Fundarò, Marina Manera, Matteo Cotta Ramusino, Marco Capelli, Alfredo Costa, Giuliano Binetti, Riccardo Bellazzi, and Alberto Redolfi. 2023. Advancing Italian biomedical information extraction with tran… find
  36. Ameet Deshpande, Partha Talukdar, and Karthik Narasimhan. 2022. When is BERT multilingual? Isolating crucial ingredients for cross-lingual transfer. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page… find
  37. Alexandra Chronopoulou, Matthew Peters, Alexander Fraser, and Jesse Dodge. 2023. AdapterSoup: Weight averaging to improve generalization of pretrained language models. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2054–2063, Dubrovnik, Croatia. Association for Comput… find
  38. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. QLoRA: Efficient finetuning of quantized LLMs. Advances in Neural Information Processing Systems, 36:10088–10115.
  39. Kevin Clark, Minh-Thang Luong, Christopher D. Manning, and Quoc Le. 2018. Semi-supervised sequence modeling with cross-view training. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1914–1925, Brussels, Belgium. Association for Computational Linguisti…
  40. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina 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,…
  41. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association f…
  42. Ning Ding, Xingtai Lv, Qiaosen Wang, Yulin Chen, Bowen Zhou, Zhiyuan Liu, and Maosong Sun. 2023a. Sparse low-rank adaptation of pre-trained language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4133–4145, Singapore. Association for Computat… find
  43. Alexis Conneau and Guillaume Lample. 2019. Cross-lingual language model pretraining. In Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu,… find
  44. William Fedus, Barret Zoph, and Noam Shazeer. 2022. Switch Transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23(120):1–39. find
  45. Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021. A survey of data augmentation approaches for NLP. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 968–988, Online. Association for Computational Lin…
  46. Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020. Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping. ArXiv:2002.06305v1 [cs]. find
  47. Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, KaShun Shum, and Tong Zhang. 2023. RAFT: Reward rAnked FineTuning for generative foundation model alignment. Transactions on Machine Learning Research.
  48. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning, pages 1126–1135. PMLR.
  49. Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Pontes Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya… find
  50. Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020. Adversarial and domain-aware bert for cross-domain sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4019–4028, Online. Association for Computational Linguistics.… find
  51. Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J. Clark, and Mehdi Rezagholizadeh. 2022. KronA: Parameter efficient tuning with kronecker adapter. ArXiv:2212.10650v1 [cs]. — [arXiv:2212.10650](https://arxiv.org/abs/2212.10650) find
  52. Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020. Active learning for BERT: An empirical study. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), page… find
  53. Matthieu François and Paul Gay. 2023. Active learning with few-shot learning for crisis management. In Proceedings of the 20th International Conference on Content-based Multimedia Indexing, CBMI ’23, pages 233–237, New York, NY, USA. Association for Computing Machinery. https://doi.org/10 .1145/361… find
  54. Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. find
  55. Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine… — Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
  56. Toronto, Canada. Association for Computational Linguistics. https://doi.org/10 .18653/v1/2023.findings-acl.189 find
  57. Ann-Sophie Gnehm, Eva Bühlmann, and Simon Clematide. 2022. Evaluation of transfer learning and domain adaptation for analyzing german-speaking job advertisements. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3892–3901, Marseille, France. European Language Res… find
  58. Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vladimir Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024. Arcee’s MergeKit: A toolkit for merging large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing:…
  59. Bofei Gao, Feifan Song, Yibo Miao, Zefan Cai, Zhe Yang, Liang Chen, Helan Hu, Runxin Xu, Qingxiu Dong, Ce Zheng, Shanghaoran Quan, Wen Xiao, Ge Zhang, Daoguang Zan, Keming Lu, Bowen Yu, Dayiheng Liu, Zeyu Cui, Jian Yang, Lei Sha, Houfeng Wang, Zhifang Sui, Peiyi Wang, Tianyu Liu, and Baobao Chang. 2… — [arXiv:2409.02795](https://arxiv.org/abs/2409.02795) find
  60. Leo Gao, John Schulman, and Jacob Hilton. 2023. Scaling laws for reward model overoptimization. In Proceedings of the 40th International Conference on Machine Learning, pages 10835–10866. PMLR.
  61. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple contrastive learning of sentence embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6894–6910, Online and Punta Cana, Dominican Republic. Association for Computational Linguistic…
  62. José Antonio Garcı́a-Dı́az, Ronghao Pan, and Rafael Valencia-Garcı́a. 2023. Leveraging zero and few-shot learning for enhanced model generality in hate speech detection in Spanish and English. Mathematics, 11(24):5004. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/math1124… — [doi:10.3390/math11245004](https://doi.org/10.3390/math11245004) ↗
  63. Felix Gaschi, Patricio Cerda, Parisa Rastin, and Yannick Toussaint. 2023. Exploring the relationship between alignment and cross-lingual transfer in multilingual transformers. In Findings of the Association for Computational Linguistics: ACL 2023, pages 3020–3042, Koustava Goswami, Lukas Lange, Jun… find
  64. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao,… — The Llama 3 Herd of Models
  65. Daniel Grießhaber, Ngoc Thang Vu, and Johannes Maucher. 2020. Low-resource text classification using domain-adversarial learning. Computer Speech & Language, 62:101056. https://doi.org/10.1016/j.csl.2019 .101056 — [doi:10.1016/j.csl.2019](https://doi.org/10.1016/j.csl.2019) ↗
  66. Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra, Mutsumi Nakamura, Arindam Mitra, Santosh Mashetty, and Chitta Baral. 2023. Instruction tuned models are quick learners. ArXiv:2306.05539 [cs]. — [arXiv:2306.05539](https://arxiv.org/abs/2306.05539) find
  67. Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020. Don’t stop pretraining: Adapt language models to domains and tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8342–8360, Online. A… find
  68. Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu. 2021. Pre-trained… find
  69. Sifre. 2022. Training compute-optimal large language models. In Advances in Neural Information Processing Systems, volume 36, pages 30016–30030.
  70. Jiwoo Hong, Noah Lee, and James Thorne. 2024. ORPO: Monolithic preference optimization without reference model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11170–11189, Miami, Florida, USA. Association for Computational Linguistics. https://doi.or… ↗
  71. Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, and Sai Qian Zhang. 2024. Parameter-efficient fine-tuning for large models: A comprehensive survey. Transactions on Machine Learning Research.
  72. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, pages 2790–2799. PMLR. find
  73. Soufiane Hayou, Nikhil Ghosh, and Bin Yu. 2024. LoRA+: Efficient low rank adaptation of large models. In Proceedings of the 41st International Conference on Machine Learning, pages 17783–17806. PMLR.
  74. Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2021. Towards a unified view of parameter-efficient transfer learning. In International Conference on Learning Representations.
  75. Jeremy Howard and Sebastian 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. h… find
  76. Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. DeBERTa: Decoding-enhanced BERT with disentangled attention. In International Conference on Learning Representations.
  77. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations.
  78. Michael A. Hedderich, Lukas Lange, Heike Adel, Jannik Strötgen, and Dietrich Klakow. 2021. A survey on recent approaches for natural language processing in low-resource scenarios. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: H…
  79. Xiyang Hu, Xinchi Chen, Peng Qi, Deguang Kong, Kunlun Liu, William Yang Wang, and Zhiheng Huang. 2023a. Language agnostic multilingual information retrieval with contrastive learning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9133–9146, Toronto, Canada. Associatio… find
  80. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, E… — LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
  81. Haoyang Huang, Tianyi Tang, Dongdong Zhang, Xin Zhao, Ting Song, Yan Xia, and Furu Wei. 2023a. Not all languages are created equal in LLMs: Improving multilingual capability by cross-lingual-thought prompting. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12365–1239…
  82. Yinqiu Huang, Min Gao, Jia Wang, Junwei Yin, Kai Shu, Qilin Fan, and Junhao Wen. 2023b. Meta-prompt based learning for low-resource false information detection. Information Processing & Management, 60(3):103279. https://doi.org/10.1016/j.ipm.2023 .103279 — [doi:10.1016/j.ipm.2023](https://doi.org/10.1016/j.ipm.2023) ↗
  83. Zhiqi Huang, Puxuan Yu, and James Allan. 2023c. Improving cross-lingual information retrieval on low-resource languages via optimal transport distillation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM ’23, pages 1048–1056, New York, NY, USA. Associ…
  84. Ayyoob Imani, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, and Hinrich Schütze. 2023. Glot500: Scaling multilingual corpora and language models to 500 languages. In Proceedings of the 61st Annual Me… find
  85. Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O’Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, and Ves Stoyanov. 2023. OPT-IML: Scaling lang…
  86. Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit. 2024. Leveraging large language models for predictive chemistry. Nature Machine Intelligence, 6(2):161–169. Nature Publishing Group. https://doi.org/10.1038/s42256 -023-00788-1 ↗
  87. Neel Jain, Ping yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2024. NEFTune: Noisy embeddings improve instruction finetuning. In The Twelfth Inter… find
  88. Mathew Huerta-Enochian and Seung Yong Ko. 2024. Instruction fine-tuning: Does prompt loss matter? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 22771–22795, Miami, Florida, USA. Association for Computational Linguistics. https://doi.org/10 .18653/v1… find
  89. Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, and Minjoon Seo. 2023. Exploring the benefits of training expert language models over instruction tuning. In Proceedings of the 40th International Conference on Machine Learning, pages 14702–14729.…
  90. Chia-Chien Hung, Lukas Lange, and Jannik Strötgen. 2023. TADA: Efficient task-agnostic domain adaptation for Transformers. In Findings of the Association for Computational Linguistics: ACL 2023, pages 487–503, Toronto, Canada. Association for Computational Linguistics. https://doi.org/10.18653 /v1/… find
  91. Michael Jantscher, Felix Gunzer, Roman Kern, Eva Hassler, Sebastian Tschauner, and Gernot Reishofer. 2023. Information extraction from German radiological reports for general clinical text and language understanding. Scientific Reports, 13(1):2353. Nature Publishing Group. https://doi.org/10.1038/s4… — [doi:10.1038/s41598-023](https://doi.org/10.1038/s41598-023) ↗
  92. Ruili Jiang, Kehai Chen, Xuefeng Bai, Zhixuan He, Juntao Li, Muyun Yang, Tiejun Zhao, Liqiang Nie, and Min Zhang. 2024. A survey on human preference learning for large language models. ArXiv:2406.11191 [cs]. — [arXiv:2406.11191](https://arxiv.org/abs/2406.11191) find
  93. Hongpeng Jin, Wenqi Wei, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu. 2023. Rethinking learning rate tuning in the era of large language models. In 2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI), pages 112–121. https://doi.org/10.1109/CogMI58952.2023.00025 — [doi:10.1109/cogmi58952.2023.00025](https://doi.org/10.1109/cogmi58952.2023.00025) ↗
  94. In Advances in Neural Information Processing Systems, volume 34, pages 1022–1035. Curran Associates, Inc. find
  95. Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Lambert Mathias, Marzieh Saeidi, Veselin Stoyanov, and Majid Yazdani. 2022. Prompt-free and efficient few-shot learning with language models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume… find
  96. Payam Karisani. 2022. Multiple-source domain adaptation via coordinated domain encoders and paired classifiers. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7):7087–7095. https://doi.org/10 .1609/aaai.v36i7.20668 find
  97. Josip Jukić and Jan Snajder. 2023. Parameter-efficient language model tuning with active learning in low-resource settings. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5061–5074, Singapore. Association for Computational Linguistics. https://doi.o… ↗
  98. Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha, Mohammad Shamsul Arefin, and Yasuhiko Morimoto. 2023. Contrastive learning for universal zero-shot NLI with cross-lingual sentence embeddings. In Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL), pages 239–25… find
  99. Damjan Kalajdzievski. 2024. Scaling laws for forgetting when fine-tuning large language models. ArXiv:2401.05605v1 [cs]. — [arXiv:2401.05605](https://arxiv.org/abs/2401.05605) find
  100. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. ArXiv:2001.08361v1 [cs, stat].
  101. Taku Kudo and John Richardson. 2018. SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 66–71, Brussels, Belgium. Associ…
  102. Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, and Christopher Manning. 2021. Mind your outliers! Investigating the negative impact of outliers on active learning for visual question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th… find
  103. Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018. Phrase-based & neural unsupervised machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 5039–5049, Brussels, Belgium. Association for Computat… find
  104. Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder. 2021. Compacter: Efficient low-rank hypercomplex adapter layers. find
  105. Moritz Laurer, Wouter van Atteveldt, Andreu Casas, and Kasper Welbers. 2024. Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and BERT-NLI. Political Analysis, 32(1):84–100. https://doi.org/10.1017/pan.2023.20 — [doi:10.1017/pan.2023.20](https://doi.org/10.1017/pan.2023.20) ↗
  106. Xiang Lisa Li and Percy 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–… find
  107. Cheolhyoung Lee, Kyunghyun Cho, and Wanmo Kang. 2019. Mixout: Effective regularization to finetune large-scale pretrained language models. In International Conference on Learning Representations. find
  108. Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. AlpacaEval: An automatic evaluator of instruction-following models. find
  109. Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Ren Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. 2024. RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback. In Proceedings of the 41st Internatio… find
  110. Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, and Anna Rumshisky. 2024. Scaling down to scale up: A guide to parameter-efficient fine-tuning. ArXiv:2303.15647 [cs].
  111. Jens Lemmens and Walter Daelemans. 2023. Combining active learning and task adaptation with BERT for cost-effective annotation of social media datasets. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 237–250, Toronto, Canada… find
  112. Brian Lester, Rami Al-Rfou, and Noah 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 Linguist…
  113. Jing Li, Billy Chiu, Shanshan Feng, and Hao Wang. 2022. Few-shot named entity recognition via meta-learning. IEEE Transactions on Knowledge and Data Engineering, 34(9):4245–4256. Conference Name: IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE .2020.3038670 — [doi:10.1109/tkde](https://doi.org/10.1109/tkde) ↗
  114. Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyano… find
  115. Tongtao Ling, Lei Chen, Yutao Lai, and Hai-Lin Liu. 2023. Evolutionary verbalizer search for prompt-based few shot text classification. In Knowledge Science, Engineering and Management, pages 279–290, Cham. Springer Nature Switzerland. https://doi.org/10 .1007/978-3-031-40292-0 23 find
  116. Guanghui Liu. 2025. glgh/awesome-llm-human -preference-datasets. Original-date: 2023-05-03T16:48:10Z. find
  117. Hao Liu, Carmelo Sferrazza, and Pieter Abbeel. 2023a. Chain of hindsight aligns language models with feedback. In International Conference on Learning Representations.
  118. Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin 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.
  119. Zhou, Quoc V. Le, Barret Zoph, Jason Wei, and Adam Roberts. 2023. The flan collection: Designing data and methods for effective instruction tuning. In Proceedings of the 40th International Conference on Machine Learning, pages 22631–22648. PMLR.
  120. Jia Liu, Yue Wang, Zhiqi Lin, Min Chen, Yixue Hao, and Long Hu. 2024a. Natural language fine-tuning. ArXiv:2412.20382 [cs]. — [arXiv:2412.20382](https://arxiv.org/abs/2412.20382) find
  121. Keming Lu, Peter Potash, Xihui Lin, Yuwen Sun, Zihan Qian, Zheng Yuan, Tristan Naumann, Tianxi Cai, and Junwei Lu. 2023. Prompt discriminative language models for domain adaptation. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 247–258, Toronto, Canada. Association f… find
  122. Meizhen Liu, Xu Guo, He Jiakai, Jianye Chen, Fengyu Zhou, and Siu Hui. 2023b. InteMATs: Integrating granularity-specific multilingual adapters for cross-lingual transfer. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5035–5049, Singapore. Association for Computation… find
  123. Wei Lu, Rachel K. Luu, and Markus J. Buehler. 2025. Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities. NPJ Computational Materials, 11(1):1–43. Nature Publishing Group. https://doi.org/10.1038 /s41524-025-… — [doi:10.1145/3583780](https://doi.org/10.1145/3583780) find
  124. Aniruddha Mahapatra, Sharmila Reddy Nangi, Aparna Garimella, and Anandhavelu N. 2022. Entity extraction in low resource domains with selective pre-training of large language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 942–951, Abu Dhabi, U… find
  125. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023d. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35. https://doi.org/10 .1145/3560815
  126. Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Scott Yih, and Madian Khabsa. 2022. UniPELT: A unified framework for parameter-efficient language model tuning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers…
  127. Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen. 2024b. DoRA: Weight-decomposed low-rank adaptation. In Proceedings of the 41st International Conference on Machine Learning, pages 32100–32121. PMLR. find
  128. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized bert pretraining approach.
  129. Yu Meng, Mengzhou Xia, and Danqi Chen. 2024. SimPO: Simple preference optimization with a reference-free reward. Advances in Neural Information Processing Systems, Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny 37:124198–124235. https://doi.org/10 .52202/079017-3946 find
  130. Zaid Alyafeai, Albert Webson, Edward Raff, and Colin Raffel. 2023b. Crosslingual generalization through multitask finetuning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15991–16111, Toronto, Canada. Association for Comput…
  131. Meta AI. 2025. The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation. find
  132. Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013. Exploiting similarities among languages for machine translation. ArXiv:1309.4168v1 [cs]. find
  133. Mohammed Muqeeth, Haokun Liu, Yufan Liu, and Colin Raffel. 2024. Learning to route among specialized experts for zero-shot generalization. In International Conference on Machine Learning. find
  134. Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022. MetaICL: Learning to learn in context. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2791–2809, Seattle, United States. A… find
  135. Saliha Muradoglu and Mans Hulden. 2022. Eeny, meeny, miny, moe. How to choose data for morphological inflection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7294–7303, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. htt… find
  136. Benjamin Minixhofer, Fabian Paischer, and Navid Rekabsaz. 2022. WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Hum…
  137. Arijit Nag, Bidisha Samanta, Animesh Mukherjee, Niloy Ganguly, and Soumen Chakrabarti. 2023. Entropy-guided vocabulary augmentation of multilingual language models for low-resource tasks. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8619–8629, Toronto, Canada. Associ… find
  138. Vincenzo Moscato, Giuseppe Napolano, Marco Postiglione, and Giancarlo Sperlı̀. 2023. Multi-task learning for few-shot biomedical relation extraction. Artificial Intelligence Reviews, 56(11):13743–13763. https://doi.org/10.1007/s10462-023-10484-6 — [doi:10.1007/s10462-023-10484-6](https://doi.org/10.1007/s10462-023-10484-6) ↗
  139. Nghia Ngo Trung, Duy Phung, and Thien Huu Nguyen. 2021. Unsupervised domain adaptation for event detection using domain-specific adapters. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4015–4025, Online. Association for Computational Linguistics. https://doi .o… find
  140. Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A. Raffel. 2023a. Scaling data-constrained language models. Advances in Neural Information Processing Systems, 36:50358–50376. find
  141. Mariana Noguti, Eduardo Vellasques, and Luiz S. Oliveira. 2023. A small claims court for the NLP: Judging legal text classification strategies with small datasets. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1840–1845. https://doi.org/10.1109/SMC53992 .2023.10… ↗
  142. Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, OpenAI, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman,… — gpt-oss-120b & gpt-oss-20b Model Card
  143. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022. Tr… — Training language models to follow instructions with human feedback
  144. Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, and Akash Srivastava. 2024. Unveiling the secret recipe: A guide for supervised fine-tuning small LLMs. A… — [arXiv:2412.13337](https://arxiv.org/abs/2412.13337) find
  145. Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn. 2024. Disentangling length from quality in direct preference optimization. In Findings of the Association for Computational Linguistics: ACL 2024, pages 4998–5017, Bangkok, Thailand. Association for Computational Linguistics. https://doi.o… ↗
  146. Amalie Pauli, Leon Derczynski, and Ira Assent. 2023. Anchoring fine-tuning of sentence transformer with semantic label information for efficient truly few-shot classification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11254–11264, Singapore. Ass… find
  147. Letian Peng, Zihan Wang, and Jingbo Shang. 2023. Less than one-shot: Named entity recognition via extremely weak supervision. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13603–13616, Singapore. Association for Computational Linguistics. https://doi .org/10.18653/v… find
  148. Oleksiy Ostapenko, Zhan Su, Edoardo Ponti, Laurent Charlin, Nicolas Le Roux, Lucas Caccia, and Alessandro Sordoni. 2024. Towards modular LLMs by building and reusing a library of LoRAs. In Proceedings of the 41st International Conference on Machine Learning, pages 38885–38904. PMLR. find
  149. Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych. 2021. AdapterFusion: Non-destructive task composition for transfer learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 487–5…
  150. Libo Qin, Qiguang Chen, Fuxuan Wei, Shijue Huang, and Wanxiang Che. 2023. Cross-lingual prompting: Improving zero-shot chain-of-thought reasoning across languages. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2695–2709, Singapore. Association for C…
  151. Jonas Pfeiffer, Sebastian Ruder, Ivan Vulić, and Edoardo Ponti. 2023. Modular deep learning. Transactions on Machine Learning Research. find
  152. Muhammad Qorib, Geonsik Moon, and Hwee Tou Ng. 2024. Are decoder-only language models better than encoder-only language models in understanding word meaning? In Findings of the Association for Computational Linguistics: ACL 2024, pages 16339–16347, Bangkok, Thailand. Association for Computational Li… find
  153. Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder. 2020. MAD-X: An adapter-based framework for multi-task cross-lingual transfer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7654–7673, Online. Association for Computational L…
  154. Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. B. Knox, Chelsea Finn, and Scott Niekum. 2024. Scaling laws for reward model overoptimization in direct alignment algorithms. Advances in Neural Information Processing Systems, 37:126207–126242. https://doi.org/10 .52202/0… find
  155. Jason Phang, Thibault Févry, and Samuel R. Bowman. 2019. Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks. ArXiv:1811.01088v2 [cs].
  156. Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio, and Siva Reddy. 2022. Combining modular skills in multitask learning. ArXiv: 2202.13914 [cs]. — [arXiv:2202.13914](https://arxiv.org/abs/2202.13914) find
  157. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728–53741.
  158. Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-task transfer learning with pretrained language models: When and why does it work? In Proceedings of the 58th Annual Meeting of the Ass…
  159. Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi. 2022. Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization…
  160. Kunxun Qi, Hai Wan, Jianfeng Du, and Haolan Chen. 2022. Enhancing cross-lingual natural language inference by prompt-learning from cross-lingual templates. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1910–1923, Dublin, Ire… find
  161. Vinay Venkatesh Ramasesh, Aitor Lewkowycz, and Ethan Dyer. 2021. Effect of scale on catastrophic forgetting in neural networks. In International Conference on Learning Representations. find
  162. Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, and Iryna Gurevych. 2021. AdapterDrop: On the efficiency of adapters in transformers. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7930–7946, Online and Punta…
  163. Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Tali Bers, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush. 2022. Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representation…
  164. Shuo Ren, Zhirui Zhang, Shujie Liu, Ming Zhou, and Shuai Ma. 2019. Unsupervised neural machine translation with SMT as posterior regularization. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1):241–248. https://doi.org/10.1609/aaai.v33i01 .3301241 — [doi:10.1609/aaai.v33i01](https://doi.org/10.1609/aaai.v33i01) ↗
  165. Timo Schick and Hinrich Schütze. 2021a. Exploiting cloze-questions for few-shot text classification and natural language inference. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 255–269, Online. Association for Com…
  166. Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac… find
  167. Timo Schick and Hinrich Schütze. 2021b. 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, Online. Asso…
  168. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. ArXiv:1707.06347 [cs]. find
  169. Hossain Shaikh Saadi, Viktor Hangya, Tobias Eder, and Alexander Fraser. 2022. Comparative analysis of cross-lingual contextualized word embeddings. In Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 64–75, Abu Dhabi, United Arab Emirates (Hybrid). Association fo… find
  170. Archit Sharma, Sedrick Keh, Eric Mitchell, Chelsea Finn, Kushal Arora, and Thomas Kollar. 2024. A critical evaluation of AI feedback for aligning large language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. find
  171. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In International Conference on Learning Representations. find
  172. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti D… — [arXiv:2409.09603](https://arxiv.org/abs/2409.09603) find
  173. Zhengyan Shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, and Aldo Lipani. 2024. Instruction tuning with loss over instructions. Advances in Neural Information Processing Systems, 37:69176–69205. https://doi.org/10.52202/079017-2210 — [doi:10.52202/079017-2210](https://doi.org/10.52202/079017-2210) ↗
  174. Wen Tai, H. T. Kung, Xin Dong, Marcus Comiter, and Chang-Fu Kuo. 2020. exBERT: Extending pre-trained models with domain-specific vocabulary under constrained training resources. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1433–1439, Online. Association for Computa… find
  175. Ken Shoemake. 1985. Animating rotation with quaternion curves. In Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’85, pages 245–254, New York, NY, USA. Association for Computing Machinery. https://doi.org/10.1145/325165.325242 — [doi:10.1145/325165.325242](https://doi.org/10.1145/325165.325242) ↗
  176. Mingxu Tao, Chen Zhang, Quzhe Huang, Tianyao Ma, Songfang Huang, Dongyan Zhao, and Yansong Feng. 2024. Unlocking the potential of model merging for low-resource languages. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8705–8720, Miami, Florida, USA. Association for… find
  177. Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. find
  178. Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel. 2020. FixMatch: Simplifying semi-supervised learning with consistency and confidence. Advances in Neural Information Processing Systems, 33:596–608. find
  179. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,… — Llama 2: Open Foundation and Fine-Tuned Chat Models
  180. Chengyu Song, Fei Cai, Mengru Wang, Jianming Zheng, and Taihua Shao. 2023. TaxonPrompt: Taxonomy-aware curriculum prompt learning for few-shot event classification. Knowledge-Based Systems, 264:110290. https://doi.org/10.1016/j.knosys.2023.110290 — [doi:10.1016/j.knosys.2023.110290](https://doi.org/10.1016/j.knosys.2023.110290) ↗
  181. Nikolaos Stylianou, Despoina Chatzakou, Theodora Tsikrika, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2023. Domain-aligned data augmentation for low-resource and imbalanced text classification. In Advances in Information Retrieval, pages 172–187, Cham. Springer Nature Switzerland. https://doi .or… — [doi:10.1007/978-3-031-28238-6](https://doi.org/10.1007/978-3-031-28238-6) find
  182. Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. 2022. LST: Ladder side-tuning for parameter and memory efficient transfer learning. Advances in Neural Information Processing Systems, 35:12991–13005. find
  183. Yi-Lin Sung, Varun Nair, and Colin A. Raffel. 2021. Training neural networks with fixed sparse masks. In Advances in Neural Information Processing Systems, volume 34, pages 24193–24205. Curran Associates, Inc. find
  184. Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubrama… — Efficient Methods for Natural Language Processing: A Survey
  185. Langlotz, Akshay Chaudhari, and John Pauly. 2023. RadAdapt: Radiology report summarization via lightweight domain adaptation of large language models. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 449–460, Toronto, Canada. Association for Computational… find
  186. Hazal Türkmen, Oguz Dikenelli, Cenk Eraslan, Mehmet Cem Çalli, and Süha Süreyya Özbek. 2023. BioBERTurk: Exploring turkish biomedical language model development strategies in low-resource setting. Journal of Healthcare Informatics Research, 7(4):433–446. https://doi.org/10.1007/s41666-023 -0014… — [doi:10.1007/s41666-023](https://doi.org/10.1007/s41666-023) ↗
  187. Lifu Tu, Jin Qu, Semih Yavuz, Shafiq Joty, Wenhao Liu, Caiming Xiong, and Yingbo Zhou. 2024. Efficiently aligned cross-lingual transfer learning for conversational tasks using prompt-tuning. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1278–1294, St. Julian’s, Malta… find
  188. Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert, Shengyi Huang, Kashif Rasul, and Quentin Gallouédec. 2020. TRL: Transformer reinforcement learning. https://github.com/huggingface/trl. ↗
  189. Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer. 2020. Exploring and predicting transferability across NLP tasks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), p…
  190. Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, and Mi Zhang. 2024. Efficient large language models: A survey. Transactions on Machine Learning Research.
  191. Lewis Tunstall, Edward Emanuel Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro Von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2024. Zephyr: Direct distillation of LM alignment. In Fir… find
  192. Faizad Ullah, Ubaid Azam, Ali Faheem, Faisal Kamiran, and Asim Karim. 2023. Comparing prompt-based and standard fine-tuning for urdu text classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6747–6754, Singapore. Association for Computational Linguistics. h… find
  193. Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek, Christian Bluethgen, Malgorzata Polacin, Wah Chiu, Jean-Benoit Delbrouck, Juan Zambrano Chaves, Curtis Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, and Jie Zhou. 2023a. Towards unifying multi-lingual and cross-l… find
  194. Minzheng Wang, Jia Cao, Qingchao Kong, and Yin Luo. 2023b. Boosting domain-specific question answering through weakly supervised self-training. In 2023 IEEE International Conference on Intelligence and Security Informatics (ISI), pages 1–6. https://doi.org/10.1109/ISI58743.2023.10297258 — [doi:10.1109/isi58743.2023.10297258](https://doi.org/10.1109/isi58743.2023.10297258) ↗
  195. Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, Jihua Kang, Jingsheng Yang, Siyuan Li, and Chunsai Du. 2023c. InstructUIE: Multi-task instruction tuning for unified information extraction. ArXiv:2304.08085 [cs]. ings of the 21st Conferen…
  196. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lho… — Transformers: State-of-the-Art Natural Language Processing
  197. Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, and Jianfeng Gao. 2022. AdaMix: Mixture-of-adaptations for parameter-efficient model tuning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5744–5760, A…
  198. Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, and Hannaneh Hajishirzi. 2023d. How far can camels go? Exploring the state of instruction tuning on open resources. Advances in Neural Information Process…
  199. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt. 2022. Model soups: Averaging weights of multiple fine-tuned models improves accuracy without increasing inferen…
  200. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2021. Finetuned language models are zero-shot learners. In International Conference on Learning Representations.
  201. Hui Wu, Yuting He, Yidong Chen, Yu Bai, and Xiaodong Shi. 2024. Improving few-shot relation extraction through semantics-guided learning. Neural Networks, 169:453–461. https://doi.org/10.1016/j.neunet .2023.10.053, PubMed: 37939534 — [doi:10.1016/j.neunet](https://doi.org/10.1016/j.neunet) ↗
  202. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837.
  203. Wen Wen, Yongbin Liu, Chunping Ouyang, Qiang Lin, and Tonglee Chung. 2021. Enhanced prototypical network for few-shot relation extraction. Information Processing & Management, 58(4):102596. https://doi.org/10.1016/j.ipm.2021.102596 — [doi:10.1016/j.ipm.2021.102596](https://doi.org/10.1016/j.ipm.2021.102596) ↗
  204. Ting-Wei Wu, Changsheng Zhao, Ernie Chang, Yangyang Shi, Pierce Chuang, Vikas Chandra, and Biing Juang. 2023. Towards zero-shot multilingual transfer for code-switched responses. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages… find
  205. Georg Wiese, Dirk Weissenborn, and Mariana Neves. 2017. Neural domain adaptation for biomedical question answering. In Proceed-Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen. 2024. LESS: Selecting influential data for targeted instruction tuning. In Proceedings of th…
  206. Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021 .emnlp-main.749 — [doi:10.18653/v1/2021](https://doi.org/10.18653/v1/2021) ↗
  207. Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020. Unsupervised data augmentation for consistency training. In Advances in Neural Information Processing Systems, volume 33, pages 6256–6268. Curran Associates, Inc.
  208. Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu. 2021. ConSERT: A contrastive framework for self-supervised sentence representation transfer. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conferenc…
  209. Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy S. Liang. 2023. Data selection for language models via importance resampling. Advances in Neural Information Processing Systems, 36:34201–34227. find
  210. Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. 2024. Contrastive preference optimization: Pushing the boundaries of LLM performance in machine translation. In Proceedings of the 41st International Conference on Machine Learning, p…
  211. Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, and Heng Ji. 2023. Unlearning bias in language models by partitioning gradients. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6032–6048. Toronto, Canada. Association for Computational Linguistics. find
  212. Mengwei Xu, Dongqi Cai, Wangsong Yin, Shangguang Wang, Xin Jin, and Xuanzhe Liu. 2025. Resource-efficient algorithms and systems of foundation models: A survey. ACM Computing Surveys, 57(5):110:1–110:39. https://doi.org/10.1145/3706418 — [doi:10.1145/3706418](https://doi.org/10.1145/3706418) ↗
  213. Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince, and Yanshuai Cao. 2021a. Optimizing deeper transformers on small datasets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Internat… find
  214. Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021b. Raise a child in large language model: Towards effective and generalizable fine-tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9514–9528, Mic…
  215. Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. 2023. RRHF: Rank responses to align language models with human feedback without tears. ArXiv:2304.05302 [cs].
  216. Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat. 2023a. When scaling meets LLM finetuning: The effect of data, model and fine-tuning method. In The Twelfth International Conference on Learning Representations. find
  217. Haojie Zhang, Ge Li, Jia Li, Zhongjin Zhang, Yuqi Zhu, and Zhi Jin. 2022a. Fine-tuning pre-trained language models effectively by optimizing subnetworks adaptively. Advances in Neural Information Processing Systems, 35:21442–21454. find
  218. Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. 2022b. Adaptive budget allocation for parameter-efficient fine-tuning. In The Eleventh International Conference on Learning Representations. find
  219. Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Ruotong Geng, Huixing Jiang, Wei Wu, and Weiran Xu. 2022. ADPL: Adversarial prompt-based domain adaptation for dialogue summarization with knowledge disentanglement. In Proceedings of the 45th International ACM SIGIR Conference on Research and Developm… — [doi:10.1145/3477495](https://doi.org/10.1145/3477495) find
  220. Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 586–595. https://doi.org/10.1109/CVPR.2018.00068 — [doi:10.1109/cvpr.2018.00068](https://doi.org/10.1109/cvpr.2018.00068) ↗
  221. Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, and Peter J. Liu. 2023b. SLiC-HF: Sequence likelihood calibration with human feedback. ArXiv:2305.10425 [cs].
  222. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and Guoyin Wang. 2024. Instruction tuning for large language models: A survey. ArXiv:2308 .10792 [cs].
  223. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. 2023. LIMA: Less is more for alignment. Advances in Neural Information Processing Systems, 36:55006–55021. find
  224. Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, and Yoav Artzi. 2021. Revisiting few-sample BERT fine-tuning. In International Conference on Learning Representations.
  225. Yue Zhang, Leyang Cui, Deng Cai, Xinting Huang, Tao Fang, and Wei Bi. 2023b. Multi-task instruction tuning of LLaMa for specific scenarios: A preliminary study on writing assistance. ArXiv:2305.13225 [cs]. — [arXiv:2305.13225](https://arxiv.org/abs/2305.13225) find
  226. Haodong Zhao, Ruifang He, Mengnan Xiao, and Jing Xu. 2023a. Infusing hierarchical guidance into prompt tuning: A parameter-efficient framework for multi-level implicit discourse relation recognition. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1…
  227. Lingyun Zhao, Lin Li, Xinhao Zheng, and Jianwei Zhang. 2021. A BERT based sentiment analysis and key entity detection approach for online financial texts. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 1233–1238. https://doi.org/10 .1109/CS… find
  228. Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 2223–2232. find
  229. Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2020. Fine-tuning language models from human preferences. ArXiv:1909.08593 [cs]. find

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