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arxiv: 2303.17651 · v2 · submitted 2023-03-30 · 💻 cs.CL · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

Self-Refine: Iterative Refinement with Self-Feedback

Aman Madaan , Niket Tandon , Prakhar Gupta , Skyler Hallinan , Luyu Gao , Sarah Wiegreffe , Uri Alon , Nouha Dziri
show 8 more authors
Shrimai Prabhumoye Yiming Yang Shashank Gupta Bodhisattwa Prasad Majumder Katherine Hermann Sean Welleck Amir Yazdanbakhsh Peter Clark
Authors on Pith no claims yet

Pith reviewed 2026-05-10 20:43 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords self-refinementiterative improvementself-feedbacklarge language modelsLLM output refinementtest-time scalingdialog generationmathematical reasoning
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The pith

Large language models can improve their own outputs by iteratively generating feedback and refinements without any training or extra models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Self-Refine, a method where an LLM first produces an output, then uses the same model to critique that output and generate a better version, repeating the process until improvements stop. This requires no supervised data, fine-tuning, or separate reward models, only the base LLM acting as generator, critic, and refiner in turn. The authors test it on seven tasks spanning dialog, reasoning, and more with GPT-3.5, ChatGPT, and GPT-4, finding that human judges and automatic scores favor the refined results, with roughly 20 percent absolute gains over plain one-step generation. A reader would care because the technique shows a straightforward way to extract more quality from existing models at inference time.

Core claim

Self-Refine demonstrates that the same LLM can generate an initial response, produce specific feedback on its shortcomings, and then produce an improved response based on that feedback, repeating the cycle as needed. When applied across dialog response generation, mathematical reasoning, and other tasks, this iterative self-correction yields outputs that both humans and metrics rate higher than the model's direct one-shot answers, with average task performance rising by about 20 percent absolute.

What carries the argument

Self-Refine, the three-step loop in which one LLM generates an output, writes feedback on it, and then rewrites the output to address the feedback, all without external supervision.

If this is right

  • Task performance rises by roughly 20 percent on average over direct generation across dialog, reasoning, and similar problems.
  • Human evaluators consistently prefer the outputs after self-refinement to the initial one-step versions.
  • The gains hold for current top models such as GPT-4 without requiring any new training data or reinforcement learning.
  • The method applies uniformly to the seven tested tasks without task-specific engineering.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Test-time iteration of this kind could serve as a lightweight substitute for additional pretraining or fine-tuning on some tasks.
  • The approach may reduce certain error types such as factual inconsistencies if the feedback step reliably catches them.
  • Combining the loop with existing prompting styles like chain-of-thought could produce further additive gains.

Load-bearing premise

The LLM must be able to produce accurate and actionable feedback on its own outputs that genuinely leads to better results rather than neutral changes or new mistakes.

What would settle it

A controlled test on any of the evaluated tasks in which multiple rounds of Self-Refine produce outputs that score no higher, or lower, than the model's standard single-pass generation on the same human or automatic metrics.

read the original abstract

Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides feedback for its output and uses it to refine itself, iteratively. Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner, and feedback provider. We evaluate Self-Refine across 7 diverse tasks, ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs. Across all evaluated tasks, outputs generated with Self-Refine are preferred by humans and automatic metrics over those generated with the same LLM using conventional one-step generation, improving by ~20% absolute on average in task performance. Our work demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using our simple, standalone approach.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 2 minor

Summary. The paper introduces Self-Refine, a training-free iterative method in which a single LLM first generates an initial output, then uses the same model to produce self-feedback on that output, and finally refines the output based on the feedback; the process can be repeated. The approach is evaluated on seven diverse tasks (dialogue, reasoning, code generation, etc.) with GPT-3.5, ChatGPT, and GPT-4, claiming that Self-Refine outputs are preferred by both human judges and automatic metrics over standard one-step generation, with an average absolute improvement of approximately 20%.

Significance. If the reported gains are shown to arise from genuine self-refinement rather than confounds, the result would be significant: it would demonstrate that current frontier LLMs can be improved at inference time through simple, standalone self-interaction without any additional training data, RL, or external models, providing a broadly applicable technique across NLP tasks.

major comments (4)
  1. [Evaluation / Results] The central empirical claim rests on the unverified assumption that the LLM produces accurate and actionable self-feedback. The manuscript provides no quantitative breakdown (e.g., human or automatic annotation of feedback correctness, error-identification rate, or adherence rate in the subsequent refinement step) in the evaluation or results sections; without this, it remains possible that the ~20% average lift arises from repeated sampling, longer context, or extra inference steps rather than iterative self-correction.
  2. [Experiments] No controls are reported for output length or total token usage. Iterative refinement typically produces longer responses; the paper does not compare against length-matched baselines or report token counts, leaving open the possibility that metric improvements (especially on tasks where verbosity correlates with quality) are partly driven by this confound rather than the refinement mechanism itself.
  3. [Results] The ~20% average improvement is presented without per-task variances, statistical significance tests, confidence intervals, or the exact number of iterations used per task and model. These details are necessary to establish that the gains are robust and not driven by a subset of tasks or unstable runs.
  4. [Method] Prompt templates for the initial generation, feedback, and refinement stages are not provided in sufficient detail (or in an appendix), which prevents exact reproduction and makes it impossible to determine whether the self-feedback prompts were carefully engineered or whether the method generalizes beyond the specific prompts used.
minor comments (2)
  1. [Abstract] The abstract states an average ~20% absolute improvement but does not specify which automatic metrics were used for each task; adding this information would improve clarity.
  2. [Related Work] Related work on self-consistency, chain-of-thought, and other test-time scaling methods is mentioned but could be expanded with more precise comparisons of computational cost and performance deltas.

Simulated Author's Rebuttal

4 responses · 0 unresolved

Thank you for your thorough and constructive review. We appreciate the feedback and will address each major comment below, proposing specific revisions to strengthen the manuscript where appropriate.

read point-by-point responses

  1. Referee: [Evaluation / Results] The central empirical claim rests on the unverified assumption that the LLM produces accurate and actionable self-feedback. The manuscript provides no quantitative breakdown (e.g., human or automatic annotation of feedback correctness, error-identification rate, or adherence rate in the subsequent refinement step) in the evaluation or results sections; without this, it remains possible that the ~20% average lift arises from repeated sampling, longer context, or extra inference steps rather than iterative self-correction.

    Authors: We agree that a direct quantitative analysis of self-feedback quality would provide stronger support for the mechanism. Although human preference judgments and automatic metric gains indicate effective refinements, we will add a new analysis subsection reporting human-annotated feedback correctness, error identification rates, and adherence in the refinement step on sampled instances from multiple tasks. To address confounds such as repeated sampling or extra steps, we will also include comparisons against best-of-n sampling baselines with matched inference budgets. revision: yes

  2. Referee: [Experiments] No controls are reported for output length or total token usage. Iterative refinement typically produces longer responses; the paper does not compare against length-matched baselines or report token counts, leaving open the possibility that metric improvements (especially on tasks where verbosity correlates with quality) are partly driven by this confound rather than the refinement mechanism itself.

    Authors: We acknowledge the importance of controlling for length and token usage. In the revision, we will report average token counts and output lengths for baseline and Self-Refine outputs across all tasks and models. We will further add length-matched baseline comparisons, for example by constraining generation length in the one-step baseline or by length-normalized evaluation. revision: yes

  3. Referee: [Results] The ~20% average improvement is presented without per-task variances, statistical significance tests, confidence intervals, or the exact number of iterations used per task and model. These details are necessary to establish that the gains are robust and not driven by a subset of tasks or unstable runs.

    Authors: We will revise the results section to include per-task scores with standard deviations, 95% confidence intervals, and statistical significance tests (paired t-tests or equivalent) between Self-Refine and baselines. We will also explicitly state the iteration counts used per task and model (typically 2–3 iterations or until convergence). revision: yes

  4. Referee: [Method] Prompt templates for the initial generation, feedback, and refinement stages are not provided in sufficient detail (or in an appendix), which prevents exact reproduction and makes it impossible to determine whether the self-feedback prompts were carefully engineered or whether the method generalizes beyond the specific prompts used.

    Authors: We apologize for the omission. The revised manuscript will include all prompt templates in full detail in a dedicated appendix, covering the exact wording for initial generation, feedback, and refinement stages for each task and model. revision: yes

Circularity Check

0 steps flagged

No significant circularity; purely empirical evaluation

full rationale

The paper introduces Self-Refine as an empirical prompting technique that uses the same LLM for generation, feedback, and refinement, then evaluates it on seven tasks against one-step baselines. No equations, derivations, fitted parameters, or first-principles predictions appear in the provided text. Claims rest on human and automatic metric comparisons showing ~20% average gains, not on any reduction of outputs to inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes that would collapse the result. The core assumption about feedback quality is an unverified empirical hypothesis tested only via downstream task metrics, which is a validity concern rather than circularity under the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that LLMs possess sufficient meta-reasoning ability to critique and improve their own outputs; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption A single LLM can generate useful, actionable feedback on its own outputs that leads to measurable improvement when used for refinement
    This assumption is required for the method to work without external supervision or training and is not derived or proven in the abstract.

pith-pipeline@v0.9.0 · 5585 in / 1229 out tokens · 62979 ms · 2026-05-10T20:43:04.617396+00:00 · methodology

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Works this paper leans on

50 extracted references · 50 canonical work pages · cited by 64 Pith papers · 10 internal anchors

  1. [1]

    Teresa M. Amabile. 1983. https://doi.org/10.1007/978-1-4612-5533-8_4 A Theoretical Framework . In The Social Psychology of Creativity , pages 65--96. Springer New York, New York, NY

    work page doi:10.1007/978-1-4612-5533-8_4 1983

  2. [2]

    Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 a . https://arxiv.org/abs/2204.05862 Training a helpful and harmless assistant with reinforcement learning from human feedback . ArXiv:2204.05862

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  3. [3]

    Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 b . Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  4. [4]

    Emery D Berger, Sam Stern, and Juan Altmayer Pizzorno. 2022. https://arxiv.org/abs/2212.07597 Triangulating Python Performance Issues with SCALENE https://arxiv.org/pdf/2212.07597.pdf . ArXiv preprint, abs/2212.07597

    work page arXiv 2022

  5. [5]

    Lawrence D Brown, T Tony Cai, and Anirban DasGupta. 2001. Interval estimation for a binomial proportion. Statistical science, 16(2):101--133

    work page 2001

  6. [6]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gr...

    work page 2020

  7. [7]

    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, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  8. [8]

    Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90\ quality

    work page 2023

  9. [9]

    Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  10. [10]

    Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, and Xiaojin Zhu. 2019. http://proceedings.mlr.press/v97/dasgupta19a.html Teaching a black-box learner . In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA , volume 97 of Proceedings of Machine Learning Research, pages 1547--1555. PMLR

    work page 2019

  11. [11]

    Wanyu Du, Zae Myung Kim, Vipul Raheja, Dhruv Kumar, and Dongyeop Kang. 2022. https://aclanthology.org/2022.in2writing-1.14 Read, revise, repeat: A system demonstration for human-in-the-loop iterative text revision . In Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022), pages 96--108, Dublin, Ireland. Ass...

    work page 2022

  12. [12]

    Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, and Ahmed Hassan Awadallah. 2021. https://doi.org/10.18653/v1/2021.naacl-main.444 NL - EDIT : Correcting semantic parse errors through natural language interaction . In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Lin...

    work page doi:10.18653/v1/2021.naacl-main.444 2021

  13. [13]

    Linda Flower and John R Hayes. 1981. A cognitive process theory of writing. College composition and communication, 32(4):365--387

    work page 1981

  14. [14]

    Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2023. https://arxiv.org/abs/2302.04166 Gptscore: Evaluate as you desire . arXiv preprint arXiv:2302.04166

    work page arXiv 2023

  15. [15]

    Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022. Pal: Program-aided language models. arXiv preprint arXiv:2211.10435

    work page Pith review arXiv 2022

  16. [16]

    Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. https://bair.berkeley.edu/blog/2023/04/03/koala/ Koala: A dialogue model for academic research . Blog post

    work page 2023

  17. [18]

    Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven C. H. Hoi. 2022 b . Coderl: Mastering code generation through pretrained models and deep reinforcement learning. ArXiv, abs/2207.01780

    work page arXiv 2022

  18. [19]

    Juncen Li, Robin Jia, He He, and Percy Liang. 2018. https://doi.org/10.18653/v1/N18-1169 Delete, retrieve, generate: a simple approach to sentiment and style transfer . In Proceedings of the 2018 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , pages 1865--187...

    work page doi:10.18653/v1/n18-1169 2018

  19. [20]

    Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020. https://doi.org/10.18653/v1/2020.findings-emnlp.165 C ommon G en: A constrained text generation challenge for generative commonsense reasoning . In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1823--1840, Online. As...

    work page doi:10.18653/v1/2020.findings-emnlp.165 2020

  20. [21]

    Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, and Yejin Choi. 2022. Rainier: Reinforced knowledge introspector for commonsense question answering. In Conference on Empirical Methods in Natural Language Processing

    work page 2022

  21. [22]

    Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi. 2022. Quark: Controllable text generation with reinforced unlearning. ArXiv, abs/2205.13636

    work page arXiv 2022

  22. [23]

    Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, and Amir Yazdanbakhsh. 2023. Learning performance-improving code edits. arXiv preprint arXiv:2302.07867

    work page arXiv 2023

  23. [24]

    Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, and Eduard Hovy. 2021. https://doi.org/10.18653/v1/2021.emnlp-main.508 Think about it! improving defeasible reasoning by first modeling the question scenario. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6291--6310, Online and Punta ...

    work page doi:10.18653/v1/2021.emnlp-main.508 2021

  24. [25]

    Shikib Mehri and Maxine Eskenazi. 2020. https://aclanthology.org/2020.sigdial-1.28 Unsupervised evaluation of interactive dialog with D ialo GPT . In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 225--235, 1st virtual meeting. Association for Computational Linguistics

    work page 2020

  25. [26]

    Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. https://arxiv.org/abs/2203.13474 Codegen: An open large language model for code with multi-turn program synthesis . ArXiv preprint, abs/2203.13474

    work page internal anchor Pith review arXiv 2022

  26. [27]

    Model index for researchers

    OpenAI . Model index for researchers. https://platform.openai.com/docs/model-index-for-researchers. Accessed: May 14, 2023

    work page 2023

  27. [28]

    OpenAI. 2022. https://beta.openai.com/docs/model-index-for-researchers Model index for researchers . Blogpost

    work page 2022

  28. [29]

    OpenAI. 2023. http://arxiv.org/abs/2303.08774 Gpt-4 technical report

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  29. [30]

    Training language models to follow instructions with human feedback

    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 E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. 2022. https://arxiv.org/abs/2203.02155 Training langua...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  30. [31]

    Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, and Jianfeng Gao. 2023. http://arxiv.org/abs/2302.12813 Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

    work page arXiv 2023

  31. [32]

    Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, and Alan W Black. 2018. https://doi.org/10.18653/v1/P18-1080 Style transfer through back-translation . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 866--876, Melbourne, Australia. Association for Computational Linguistics

    work page doi:10.18653/v1/p18-1080 2018

  32. [33]

    Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2022. https://arxiv.org/abs/2210.03350 Measuring and narrowing the compositionality gap in language models . arXiv preprint arXiv:2210.03350

    work page arXiv 2022

  33. [34]

    Ruchir Puri, David Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss. 2021. https://arxiv.org/abs/2105.12655 Codenet: A large-scale ai for code dataset for learning a diver...

    work page arXiv 2021

  34. [35]

    Machel Reid and Graham Neubig. 2022. Learning to model editing processes. arXiv preprint arXiv:2205.12374

    work page arXiv 2022

  35. [37]

    William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022 b . https://arxiv.org/abs/2206.05802 Self-critiquing models for assisting human evaluators . ArXiv:2206.05802

    work page arXiv 2022

  36. [38]

    J \'e r \'e my Scheurer, Jon Ander Campos, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. 2022. https://arxiv.org/abs/2204.14146 Training language models with natural language feedback . ArXiv:2204.14146

    work page arXiv 2022

  37. [39]

    Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. 2022 a . https://doi.org/10.48550/ARXIV.2208.11663 Peer: A collaborative language model

    work page doi:10.48550/arxiv.2208.11663 2022

  38. [40]

    Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. 2022 b . Peer: A collaborative language model. ArXiv, abs/2208.11663

    work page arXiv 2022

  39. [41]

    Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023. http://arxiv.org/abs/2303.11366 Reflexion: an autonomous agent with dynamic memory and self-reflection

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  40. [42]

    Herbert A. Simon. 1962. http://www.jstor.org/stable/985254 The architecture of complexity . Proceedings of the American Philosophical Society, 106(6):467--482

    work page 1962

  41. [43]

    Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. https://proceedings.neurips.cc/paper/2020/file/1f89885d556929e98d3ef9b86448f951-Paper.pdf Learning to summarize with human feedback . In Advances in Neural Information Processing Systems, volume 33, pages 3008--3021. C...

    work page 2020

  42. [44]

    Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan. 2023. Principle-driven self-alignment of language models from scratch with minimal human supervision. arXiv preprint arXiv:2305.03047

    work page arXiv 2023

  43. [45]

    Niket Tandon, Aman Madaan, Peter Clark, Keisuke Sakaguchi, and Yiming Yang. 2021. Interscript: A dataset for interactive learning of scripts through error feedback. arXiv preprint arXiv:2112.07867

    work page arXiv 2021

  44. [46]

    Niket Tandon, Aman Madaan, Peter Clark, and Yiming Yang. 2022. Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 339--352

    work page 2022

  45. [47]

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth \'e e Lacroix, Baptiste Rozi \`e re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  46. [48]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain of Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903. arXiv preprint arXiv:2201.11903

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  47. [49]

    Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. 2022. Generating sequences by learning to self-correct. arXiv preprint arXiv:2211.00053

    work page arXiv 2022

  48. [50]

    Kevin Yang, Nanyun Peng, Yuandong Tian, and Dan Klein. 2022. Re3: Generating longer stories with recursive reprompting and revision. In Conference on Empirical Methods in Natural Language Processing

    work page 2022

  49. [51]

    Michihiro Yasunaga and Percy Liang. 2020. http://arxiv.org/abs/2005.10636 Graph-based, self-supervised program repair from diagnostic feedback . 37th Int. Conf. Mach. Learn. ICML 2020, PartF168147-14:10730--10739

    work page arXiv 2020

  50. [52]

    Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems, 28

    work page 2015