---
title: "Who cites AI safety via debate — 81 papers of our library"
description: "81 papers of the Elamur Research library cite «AI safety via debate», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/irving-2018-ai-safety-debate/cited-by/"
---
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# Who cites «AI safety via debate»

81 papers of our library cite it (of the 149,439 whose bibliographies we read) · 38 with a page here, the most cited first · [← back to «AI safety via debate»](https://md.research.elamur.ai/free/en/papers/irving-2018-ai-safety-debate/)

1. [**Training language models to follow instructions with human feedback arXiv:2203.02155**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,224 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
2. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
3. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
4. [**Constitutional AI: Harmlessness from AI Feedback arXiv:2212.08073**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 1,028 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
5. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark arXiv:2311.12022**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 1,018 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
6. [**WebGPT: Browser-assisted question-answering with human feedback arXiv:2112.09332**Reiichiro Nakano, Jacob Hilton, Suchir Balaji, et al.*2021 · 721 citations*](https://md.research.elamur.ai/free/en/papers/nakano-2021-webgpt-browser-assisted-question-answering/)
7. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate arXiv:2305.14325**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 587 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
8. [**Towards Understanding Sycophancy in Language Models arXiv:2310.13548**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 384 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
9. [**Improving alignment of dialogue agents via targeted human judgements arXiv:2209.14375**Amelia Glaese, Nat McAleese, Maja Trębacz, et al.*2022 · 362 citations*](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/)
10. [**Solving math word problems with process- and outcome-based feedback arXiv:2211.14275**Jonathan Uesato, Nate Kushman, Ramana Kumar, et al.*2022 · 355 citations*](https://md.research.elamur.ai/free/en/papers/uesato-2022-solving-math-word-problems-process/)
11. [**Discovering Language Model Behaviors with Model-Written Evaluations arXiv:2212.09251**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/)
12. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
13. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment arXiv:2304.06767**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
14. [**Scalable agent alignment via reward modeling: a research direction arXiv:1811.07871**Jan Leike, David Krueger, Tom Everitt, et al.*2018 · 227 citations*](https://md.research.elamur.ai/free/en/papers/leike-2018-scalable-agent-alignment-reward-modeling/)
15. [**Teaching language models to support answers with verified quotes arXiv:2203.11147**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 218 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
16. [**Recursively Summarizing Books with Human Feedback arXiv:2109.10862**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 208 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/)
17. [**Will we run out of data? Limits of LLM scaling based on human-generated data arXiv:2211.04325**Pablo Villalobos, Anson Ho, Jaime Sevilla, et al.*2022 · 198 citations*](https://md.research.elamur.ai/free/en/papers/villalobos-2022-will-run-out-data-limits/)
18. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
19. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision arXiv:2312.09390**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
20. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher arXiv:2308.06463**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/)
21. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/)
22. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
23. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
24. [**Measuring Progress on Scalable Oversight for Large Language Models arXiv:2211.03540**Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/bowman-2022-measuring-progress-scalable-oversight-large/)
25. [**Debating with More Persuasive LLMs Leads to More Truthful Answers arXiv:2402.06782**Akbir Khan, John Hughes, Dan Valentine, et al.*2024 · 101 citations*](https://md.research.elamur.ai/free/en/papers/khan-2024-debating-more-persuasive-llms-leads/)
26. [**Training Socially Aligned Language Models on Simulated Social Interactions arXiv:2305.16960**Ruibo Liu, Ruixin Yang, Chenyan Jia, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-training-socially-aligned-language-models/)
27. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
28. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla arXiv:2307.09458**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/)
29. [**Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback arXiv:2303.05453**Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-personalisation-within-bounds-risk-taxonomy/)
30. **[Finding Generalizable Evidence by Learning to Convince Q&A Models](https://md.research.elamur.ai/free/en/papers/perez-2019-finding-generalizable-evidence-learning-convince/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Siddharth Karamcheti, Rob Fergus, et al.*2019 · 43 citations*
31. **[Multiple LLM Agents Debate for Equitable Cultural Alignment](https://md.research.elamur.ai/free/en/papers/ki-2025-multiple-llm-agents-debate-equitable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dayeon Ki, Rachel Rudinger, Tianyi Zhou, et al.*2025 · 10 citations*
32. **[A pragmatic account of the weak evidence effect](https://md.research.elamur.ai/free/en/papers/barnett-2021-pragmatic-account-weak-evidence-effect/) [Open Mind](https://md.research.elamur.ai/free/en/venues/open-mind/)**Samuel A. Barnett, Thomas L. Griffiths, Robert D. Hawkins*2021 · 7 citations*
33. **[AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-agenticeval-agentic-self-evolving-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixu Wang, Xin Wang, Yang Yao, et al.*2026 · 1 citation*
34. **[ARGSBASE: A Multi-Agent Interface for Structured Human–AI Deliberation](https://md.research.elamur.ai/free/en/papers/turkstra-2026-argsbase-multi-agent-interface-structured/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Frieso Turkstra, Sara Nabhani, Khalid Al Khatib*2026 · 1 citation*
35. **[ART: Adaptive Reasoning Trees for Explainable Claim Verification](https://md.research.elamur.ai/free/en/papers/wadhwa-2026-art-adaptive-reasoning-trees-explainable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, et al.*2026 · 1 citation*
36. **[Agentic Oversight via Dialectic Reasoning](https://md.research.elamur.ai/free/en/papers/ranaldi-2026-agentic-oversight-dialectic-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonardo Ranaldi, Federico Ranaldi*2026*
37. **[NP-Hard Lower Bound Complexity for Semantic Self-Verification](https://md.research.elamur.ai/free/en/papers/young-2026-np-hard-lower-bound-complexity/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Robin Young*2026*
38. **[Tandem Training for Language Models](https://md.research.elamur.ai/free/en/papers/west-2026-tandem-training-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Robert West, Ashton Anderson, Ece Kamar, et al.*2026*

43 more papers of our library cite it; they have no page here yet.

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