Augmented Language Models: a Survey
Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2302.07842
Summary
Large language models still hallucinate, slip on arithmetic and multi-step inference, and need huge parameter counts to memorise knowledge. The authors attribute this to their reliance on one parametric network and a limited context window. This Meta AI survey (one co-author is at Universitat Pompeu Fabra) maps research on Augmented Language Models (ALMs). These models keep the standard missing-token prediction objective but enrich their context in two ways. One is reasoning, meaning splitting a hard problem into easier subtasks. The other is tools: external modules such as retrievers, search engines, code interpreters or robotic arms, whose outputs are fed back into the context. The paper defines reasoning, tool and act. It then sorts the literature along three axes: how reasoning is elicited, which tools and actions are used, and whether these behaviours are hand-designed heuristics or learned. It proposes no new model or dataset. The authors argue ALMs could reduce interpretability, consistency and scaling problems. However, most methods still depend on human annotation, and few combine reasoning with tools.
Key points
- Structure of the taxonomy:
- Reasoning: prompting-based elicitation, recursive prompting, and explicit training to reason.
- Tools and acting: calling other models, information retrieval (retrieval-augmented LMs, querying search engines, web navigation), symbolic modules and code interpreters, and acting in virtual or physical environments.
- Learning: supervision versus reinforcement learning.
- Retrieval-augmented models: systems such as Borgeaud et al. (2022) and Izacard et al. (2022) can match some much larger LMs with fewer parameters.
- Compositionality gap (Press et al., 2022): a model may know a person's birth and death dates yet still compute their age incorrectly.
- Claimed benefits and caveats: the claimed benefits are truthfulness, uncertainty estimation, interpretability and broader capabilities. There are caveats, though:
- Evidence that retrieval improves factuality is scarce (Krishna et al., 2021).
- Chain-of-thought can produce correct answers from meaningless intermediate steps (Lewkowycz et al., 2022).
- Ethics and open problems:
- The authors separate passive tools (which only gather information) from active ones (which act without human validation) and flag the latter as riskier.
- Fully self-supervised augmentation remains an open question.
Cited by
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
- Mind2Web: Towards a Generalist Agent for the Web
- ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
- Evaluating Large Language Models: A Comprehensive Survey
- ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
- C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
- Challenges and Applications of Large Language Models
- Language Models can Solve Computer Tasks
- API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
- Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
- GAIA: a benchmark for General AI Assistants
- LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
- GPT-4 Technical Report
- Reasoning with Language Model Prompting: A Survey
- Tool Learning with Foundation Models
- Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
- MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
- Executable Code Actions Elicit Better LLM Agents
- The Rise and Potential of Large Language Model Based Agents: A Survey
- Benchmarking Retrieval-Augmented Generation for Medicine
- The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
- Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Cite
@misc{mialon2023augmented,
title = {{Augmented Language Models: a Survey}},
author = {Mialon, Grégoire and Dessì, Roberto and Lomeli, Maria and Nalmpantis, Christoforos and Pasunuru, Ram and Raileanu, Roberta and Rozière, Baptiste and Schick, Timo and Dwivedi-Yu, Jane and Celikyilmaz, Asli and Grave, Edouard and LeCun, Yann and Scialom, Thomas},
year = {2023},
doi = {10.48550/arxiv.2302.07842},
eprint = {2302.07842},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2302.07842}
}References
- Leonard Adolphs, Benjamin Boerschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, and Yannic Kilcher. Boosting search engines with interactive agents. Transactions on Machine Learning Research (TMLR), 2022. find
- Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al. Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691, 2022. find our card
- Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al. Solving rubik’s cube with a robot hand. arXiv preprint arXiv:1910.07113, 2019. find
- Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems (NeurIPS), 2022. find our card
- Daniel Andor, Luheng He, Kenton Lee, and Emily Pitler. Giving BERT a calculator: Finding operations and arguments with reading comprehension. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Proce… find
- Akari Asai, Xinyan Yu, Jungo Kasai, and Hannaneh Hajishirzi. One question answering model for many languages with cross-lingual dense passage retrieval. Advances in Neural Information Processing Systems (NeurIPS), 2021. find
- Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, and Wen-tau Yih. Task-aware retrieval with instructions. arXiv preprint arXiv:2211.09260, 2022. find
- Lalit R. Bahl, Frederick Jelinek, and Robert L. Mercer. A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5(2):179–190, 1983. find
- Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022. find our card
- Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sandra Mitts, Adithya Renduchintala, Stephen Roller, Dirk Rowe,… find
- Satanjeev Banerjee and Alon Lavie. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pages 65–72. Association for Computational… find our card
- Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cas… find
- Sergey Brin. Extracting patterns and relations from the world wide web. In The World Wide Web and Databases, pages 172–183. Springer Berlin Heidelberg, 1999. find
- Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022. find
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla 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,… find
- Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, and Wei Wang. Ask the right questions: Active question reformulation with reinforcement learning. International Conference on Learning Representations (ICLR), 2018. find
- Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding large language models in interactive environments with online reinforcement learning, 2023. find
- Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. Reading wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051, 2017. find our card
- Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhai… find our card
- Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, 2022. find our card
- Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emi… find
- Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems (NeurIPS), 2017. find
- Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416, 2022. find
- Christopher Clark and Matt Gardner. Simple and effective multi-paragraph reading comprehension. arXiv preprint arXiv:1710.10723, 2017. find our card
- Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. find our card
- Deborah Cohen, Moonkyung Ryu, Yinlam Chow, Orgad Keller, Ido Greenberg, Avinatan Hassidim, Michael Fink, Yossi Matias, Idan Szpektor, Craig Boutilier, et al. Dynamic planning in open-ended dialogue using reinforcement learning. arXiv preprint arXiv:2208.02294, 2022. — [arXiv:2208.02294](https://arxiv.org/abs/2208.02294) find
- Antonia Creswell and Murray Shanahan. Faithful reasoning using large language models. arXiv preprint arXiv:2208.14271, 2022. find
- Antonia Creswell, Murray Shanahan, and Irina Higgins. Selection-inference: Exploiting large language models for interpretable logical reasoning. arXiv preprint arXiv:2205.09712, 2022. find
- Ishita Dasgupta, Andrew K Lampinen, Stephanie CY Chan, Antonia Creswell, Dharshan Kumaran, James L McClelland, and Felix Hill. Language models show human-like content effects on reasoning. arXiv preprint arXiv:2207.07051, 2022. find
- Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, and Rob Fergus. Collaborating with language models for embodied reasoning, 2023. find
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. find
- Pierre L Dognin, Inkit Padhi, Igor Melnyk, and Payel Das. Regen: Reinforcement learning for text and knowledge base generation using pretrained language models. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. find
- Chris Donahue, Mina Lee, and Percy Liang. Enabling language models to fill in the blanks. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2020. find
- Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, et al. A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level. Proceedings of the National… find
- Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. Compositional semantic parsing with large language models. arXiv preprint arXiv:2209.15003, 2022. find
- Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner. Successive prompting for decomposing complex questions. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. find
- Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models, 2022. find
- Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, and Dimitris Papailiopoulos. Looped transformers as programmable computers. arXiv preprint arXiv:2301.13196, 2023. find
- Amelia Glaese, Nat McAleese, Maja Trębacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, Lucy Campbell-Gillingham, Jonathan Uesato, Po-Sen Huang, Ramona Comanescu, Fan Yang, Abigail See, Sumanth Dathathri, Rory Greig, Charlie Chen, Doug Fr… find our card
- Yoav Goldberg. Some remarks on large language models, https://gist.github.com/yoavg/59d174608e92e845c8994ac2e234c8a9. Edouard Grave, Armand Joulin, and Nicolas Usunier. Improving neural language models with a continuous cache. In International Conference on Learning Representations (ICLR), 2017. 202… ↗
- URL Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In 2017 IEEE international conference on robotics and automation (ICRA), pages 3389–3396, 2017. find
- Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, and Dilek Hakkani-Tur. Learning to navigate the web. International Conference on Learning Representations (ICLR), 2019. find
- Izzeddin Gur, Natasha Jaques, Kevin Malta, Manoj Tiwari, Honglak Lee, and Aleksandra Faust. Adversarial environment generation for learning to navigate the web. arXiv preprint arXiv:2103.01991, 2021. — [arXiv:2103.01991](https://arxiv.org/abs/2103.01991) find
- Izzeddin Gur, Ofir Nachum, Yingjie Miao, Mustafa Safdari, Austin Huang, Aakanksha Chowdhery, Sharan Narang, Noah Fiedel, and Aleksandra Faust. Understanding html with large language models. arXiv preprint arXiv:2210.03945, 2022. find
- Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International Conference on Machine Learning (ICML), 2020. find
- Yaru Hao, Haoyu Song, Li Dong, Shaohan Huang, Zewen Chi, Wenhui Wang, Shuming Ma, and Furu Wei. Language models are general-purpose interfaces. arXiv preprint arXiv:2206.06336, 2022. — [arXiv:2206.06336](https://arxiv.org/abs/2206.06336) find
- Brett K Hayes, Evan Heit, and Caren M Rotello. Memory, reasoning, and categorization: parallels and common mechanisms. Frontiers in Psychology, 5:529, 2014. find
- Hangfeng He, Hongming Zhang, and Dan Roth. Rethinking with retrieval: Faithful large language model inference. arXiv preprint arXiv:2301.00303, 2022. find
- Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In Advances in Neural Information Processing Systems (NeurIPS), 2021. find our card
- Namgyu Ho, Laura Schmid, and Se-Young Yun. Large language models are reasoning teachers, 2022. find our card
- Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022. find
- Jie Huang and Kevin Chen-Chuan Chang. Towards reasoning in large language models: A survey. arXiv preprint arXiv:2212.10403, 2022. find our card
- Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. arXiv preprint arXiv:2201.07207, 2022a. find our card
- Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al. Inner monologue: Embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608, 2022b. find
- Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, and Timothy Lillicrap. A data-driven approach for learning to control computers. In International Conference on Machine Learning (ICML), 2022. find
- 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. Opt-iml: Scaling language m… find our card
- Gautier Izacard and Edouard Grave. Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282, 2020. find our card
- Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. Atlas: Few-shot learning with retrieval augmented language models. arXiv preprint arXiv:2208.03299, 2022. find
- Albert Q Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, and Guillaume Lample. Draft, sketch, and prove: Guiding formal theorem provers with informal proofs. arXiv preprint arXiv:2210.12283, 2022. find our card
- Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. How can we know what language models know? Transactions of the Association for Computational Linguistics, 8, 2020. find our card
- Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny… find
- Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al. Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation. arxiv e-prints, page. arXiv preprint arXiv:1806.10… find
- Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, et al. Measuring compositional generalization: A comprehensive method on realistic data. In International Conference on Learning Repres… find
- Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. Generalization through Memorization: Nearest Neighbor Language Models. In International Conference on Learning Representations (ICLR), 2020. find
- Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. Unifiedqa: Crossing format boundaries with a single qa system. arXiv preprint arXiv:2005.00700, 2020. find our card
- Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. Decomposed prompting: A modular approach for solving complex tasks. arXiv preprint arXiv:2210.02406, 2022. find
- W Bradley Knox and Peter Stone. Tamer: Training an agent manually via evaluative reinforcement. In 2008 7th IEEE international conference on development and learning, pages 292–297. find
- Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems (NeurIPS), 2022. find our card
- Mojtaba Komeili, Kurt Shuster, and Jason Weston. abs/2107.07566, 2021. — [arXiv:2107.07566](https://arxiv.org/abs/2107.07566) find
- Kalpesh Krishna, Aurko Roy, and Mohit Iyyer. Hurdles to progress in long-form question answering. arXiv preprint arXiv:2103.06332, 2021. find our card
- Sawan Kumar and Partha Talukdar. Reordering examples helps during priming-based few-shot learning. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4507–4518, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.findings-acl.395. U… find
- Brenden Lake and Marco Baroni. Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. In International conference on machine learning, pages 2873– 2882. PMLR, 2018. find
- Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, Xavier Martinet, Amaury Hayat, Gabriel Ebner, Aurélien Rodriguez, and Timothée Lacroix. Hypertree proof search for neural theorem proving. In Advances in Neural Information Processing Systems (NeurIPS), 2022. find our card
- Internet-augmented dialogue generation. ArXiv, Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. Internet-augmented language models through few-shot prompting for open-domain question answering, 2022. URL https://arxiv.org/abs/2203.05115. ↗ our card
- Yann LeCun. A path towards autonomous machine intelligence, 2022. find
- Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter. Learning quadrupedal locomotion over challenging terrain. Science robotics, 5(47):eabc5986, 2020. find
- Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. Latent retrieval for weakly supervised open domain question answering. arXiv preprint arXiv:1906.00300, 2019. find our card
- Hector Levesque, Ernest Davis, and Leora Morgenstern. The winograd schema challenge. In Thirteenth international conference on the principles of knowledge representation and reasoning, 2012. find
- Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Advances in Neural Information Processing… find
- Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra. Solving quantitative reasoning problems with language models, 2022. find our card
- Belinda Li, Jane Yu, Madian Khabsa, Luke Zettlemoyer, Alon Halevy, and Jacob Andreas. Quantifying adaptability in pre-trained language models with 500 tasks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolog… find
- Shuang Li, Xavier Puig, Yilun Du, Clinton Wang, Ekin Akyurek, Antonio Torralba, Jacob Andreas, and Igor Mordatch. Pre-trained language models for interactive decision-making. arXiv preprint arXiv:2202.01771, 2022b. find
- Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto. Diffusion-lm improves controllable text generation. arXiv preprint arXiv:2205.14217, 2022c. find our card
- Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng. Code as policies: Language model programs for embodied control. arXiv preprint arXiv:2209.07753, 2022. find
- Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74–81, 2004. find
- Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, and Yejin Choi. Rainier: Reinforced knowledge introspector for commonsense question answering. arXiv preprint arXiv:2210.03078, 2022a. find
- Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, and Andrew M Dai. Mind’s eye: Grounded language model reasoning through simulation. arXiv preprint arXiv:2210.05359, 2022b. find
- Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 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 8… find our card
- Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. Sparse, Dense, and Attentional Representations for Text Retrieval. Transactions of the Association for Computational Linguistics, 9:329–345, 04 2021. ISSN 2307-387X. doi: 10.1162/tacl\_a\_00369. URL https://doi.org/10.1162/tacl\_a\_0036… ↗ our card
- James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David L Roberts, Matthew E Taylor, and Michael L Littman. Interactive learning from policy-dependent human feedback. In International Conference on Machine Learning, pages 2285–2294. PMLR, 2017. find
- John McCarthy et al. Programs with common sense. RLE and MIT computation center Cambridge, MA, USA, 1960. find
- Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147, 2022. find our card
- Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. In International Conference on Learning Representations (ICLR), 2017. find our card
- Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. Multi-hop reading comprehension through question decomposition and rescoring. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6097–6109, 2019. find
- Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. Rethinking the role of demonstrations: What makes in-context learning work?, 2022. URL https://arxiv.org/abs/2202.12837. ↗ our card
- Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. Natural instructions: Benchmarking generalization to new tasks from natural language instructions. arXiv preprint arXiv:2104.08773, 2021. find
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis H… find
- Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy P. Lillicrap, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volum… find
- Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. find our card
- Rodrigo Nogueira and Kyunghyun Cho. Task-oriented query reformulation with reinforcement learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 574–583, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi: 10.18653/v… find
- Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. Show your work: Scratchpads for intermediate computation with language models. arXiv preprint arXiv:2112.001… find our card
- Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. 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 Christiano, Jan Leike, and Ryan Lowe. Training la… find
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2002a. find our card
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318, 2002b. find our card
- Aaron Parisi, Yao Zhao, and Noah Fiedel. arXiv:2205.12255, 2022. find our card
- Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela. Unsupervised question decomposition for question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. find
- Maja Popović. chrF++: words helping character n-grams. In Proceedings of the Second Conference on Machine Translation, pages 612–618. Association for Computational Linguistics, 2017. find our card
- Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models, 2022. find
- Jing Qian, Hong Wang, Zekun Li, Shiyang Li, and Xifeng Yan. Limitations of language models in arithmetic and symbolic induction, 2022. find
- Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen. Reasoning with language model prompting: A survey, 2022. find our card
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners, 2019. find
- Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen… find
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research (JMLR), 2020. find
- Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXi… find our card
- Stephen Robertson and Hugo Zaragoza. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc, 2009. find our card
- Talm: Tool augmented language models. arXiv preprint Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan C… find our card
- Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. Peer: A collaborative language model. arXiv preprint arXiv:2208.11663, 2022. find
- Timo Schick, Jane Dwivedi-Yu, Roberto Dessì†, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023. find our card
- John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. find
- Thomas Scialom, Tuhin Chakrabarty, and Smaranda Muresan. Continual-t0: Progressively instructing 50+ tasks to language models without forgetting. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. find
- Tianxiao Shen, Victor Quach, Regina Barzilay, and Tommi Jaakkola. Blank language models. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. find
- Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, and Percy Liang. World of bits: An open-domain platform for web-based agents. In International Conference on Machine Learning (ICML), 2017. find
- Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. Distilling multi-step reasoning capabilities of large language models into smaller models via semantic decompositions. arXiv preprint arXiv:2212.00193, 2022. find our card
- Kurt Shuster, Mojtaba Komeili, Leonard Adolphs, Stephen Roller, Arthur Szlam, and Jason Weston. Language models that seek for knowledge: Modular search & generation for dialogue and prompt completion. arXiv preprint arXiv:2203.13224, 2022a. find
- Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Boureau, Melanie Kambadur, and Jason Weston. Blenderbot 3: a deployed conversational agent… find our card
- David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484–489, 2016. find
- Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, and Sergey Levine. Offline rl for natural language generation with implicit language q learning. arXiv preprint arXiv:2206.11871, 2022. — [arXiv:2206.11871](https://arxiv.org/abs/2206.11871) find
- Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022. find our card
- Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. In Advances in Neural Information Processing Systems (NeurIPS), 2020. find
- Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018. find
- Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei. Challenging big-bench tasks and whether chain-of-thought can solve them, 2022. URL https://arxiv.org/abs/2210.09261. ↗ our card
- Alon Talmor and Jonathan Berant. The web as a knowledge-base for answering complex questions. In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018. find
- Yi Tay, Mostafa Dehghani, Vinh Q Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, and Donald Metzler. Unifying language learning paradigms. arXiv preprint arXiv:2205.05131, 2022. find our card
- Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085, 2022. find our card
- Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al. Open-ended learning leads to generally capable agents. arXiv preprint arXiv:2107.12808, 2021. — [arXiv:2107.12808](https://arxiv.org/abs/2107.12808) find
- Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzho… find our card
- Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, and Armen Aghajanyan. Memorization without overfitting: Analyzing the training dynamics of large language models. In Advances in Neural Information Processing Systems (NeurIPS), 2022. find our card
- Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, and Doina Precup. Androidenv: a reinforcement learning platform for android. arXiv preprint arXiv:2105.13231, 2021. — [arXiv:2105.13231](https://arxiv.org/abs/2105.13231) find
- Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509, 2022. find our card
- Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350–354, 2019. find
- Boshi Wang, Xiang Deng, and Huan Sun. Iteratively prompt pre-trained language models for chain of thought. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022a. find
- Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. Behavior cloned transformers are neurosymbolic reasoners. arXiv preprint arXiv:2210.07382, 2022b. find
- Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. Advances in Neural Information Processing Systems (NeurIPS), 2022c. find our card
- Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Ma… find
- Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents, 2023. URL https://arxiv.org/abs/2302.01560. ↗ our card
- Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone. Deep tamer: Interactive agent shaping in high-dimensional state spaces. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 1, 2018. find
- Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. International Conference on Learning Representations (ICLR), 2022a. find
- Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. Transactions on Machine Learning Research (TMLR), 2022b. find our card
- Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903, 2022c. find our card
- Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. Neural text generation with unlikelihood training. In International Conference on Learning Representations (ICLR), 2020. find our card
- Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3):229–256, 1992. find
- Jeff Wu, Long Ouyang, Daniel M Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano. Recursively summarizing books with human feedback. arXiv preprint arXiv:2109.10862, 2021. find our card
- Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai. Promptchainer: Chaining large language model prompts through visual programming. In CHI Conference on Human Factors in Computing Systems Extended Abstracts, pages 1–10, 2022a. find
- Tongshuang Wu, Michael Terry, and Carrie Jun Cai. Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. In CHI Conference on Human Factors in Computing Systems, pages 1–22, 2022b. find
- Yuhuai Wu, Albert Q Jiang, Wenda Li, Markus N Rabe, Charles Staats, Mateja Jamnik, and Christian Szegedy. Autoformalization with large language models. Advances in Neural Information Processing Systems (NeurIPS), 2022c. find our card
- Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, and Gaurav Singh Tomar. Conqrr: Conversational query rewriting for retrieval with reinforcement learning. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022d. find
- Ted Xiao, Harris Chan, Pierre Sermanet, Ayzaan Wahid, Anthony Brohan, Karol Hausman, Sergey Levine, and Jonathan Tompson. Robotic skill acquisition via instruction augmentation with vision-language models. arXiv preprint arXiv:2211.11736, 2022. — [arXiv:2211.11736](https://arxiv.org/abs/2211.11736) find
- Jing Xu, Megan Ung, Mojtaba Komeili, Kushal Arora, Y-Lan Boureau, and Jason Weston. Learning new skills after deployment: Improving open-domain internet-driven dialogue with human feedback, 2022. find
- Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, and Diyi Yang. Seqzero: Few-shot compositional semantic parsing with sequential prompts and zero-shot models. Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022a. find
- Kevin Yang, Dan Klein, Nanyun Peng, and Yuandong Tian. Doc: Improving long story coherence with detailed outline control. arXiv preprint arXiv:2212.10077, 2022b. find
- Kevin Yang, Nanyun Peng, Yuandong Tian, and Dan Klein. Re3: Generating longer stories with recursive reprompting and revision. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022c. find
- Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents. Advances in Neural Information Processing Systems (NeurIPS), 2022a. find our card
- Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022b. find our card
- David Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 1995. find our card
- Ping Yu, Tianlu Wang, Olga Golovneva, X Alkhamissy, Gargi Ghosh, Mona Diab, and Asli Celikyilmaz. Alert: Adapting language models to reasoning tasks. arXiv preprint arXiv:2212.08286, 2022. — [arXiv:2212.08286](https://arxiv.org/abs/2212.08286) find
- Eric Zelikman, Jesse Mu, Noah D Goodman, and Yuhuai Tony Wu. Star: Self-taught reasoner bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems (NeurIPS), 2022. find
- Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. Socratic models: Composing zero-shot multimodal reasoning with language, 2022. URL https://arx… find our card
- Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models, 2023. find our card
- Victor Zhong, Caiming Xiong, and Richard Socher. Seq2SQL: Generating structured queries from natural language using reinforcement learning, 2018. URL https://openreview.net/forum?id=Syx6bz-Ab. ↗
- Zexuan Zhong, Tao Lei, and Danqi Chen. Training language models with memory augmentation. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. find
- Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625, 2022. find our card
- Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. find