---
title: "Ethan Perez — all works — Elamur Research"
description: "All 33 works of Ethan Perez in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/ethan-perez/works/"
---
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# Ethan Perez — all works

33 works · [← Ethan Perez](https://md.research.elamur.ai/free/en/authors/ethan-perez/)

1. [**Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks arXiv:2005.11401**Patrick Lewis, Ethan Perez, Aleksandra Piktus, et al.*2020 · 6,256 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/)
2. **[Language Models (Mostly) Know What They Know ↗](https://arxiv.org/abs/2207.05221) arXiv:2207.05221 · no summary yet**Saurav Kadavath et al.*2022 · 827 citations*
3. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 700 citations*
4. **[ELI5: Long Form Question Answering](https://md.research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Angela Fan, Yacine Jernite, Ethan Perez, et al.*2019 · 695 citations*
5. **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 586 citations*
6. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned arXiv:2209.07858**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 485 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
7. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
8. [**FiLM: Visual Reasoning with a General Conditioning Layer arXiv:1709.07871**Ethan Perez, Florian Strub, Harm de Vries, et al.*2017 · 400 citations*](https://md.research.elamur.ai/free/en/papers/perez-2017-film-visual-reasoning-general-conditioning/)
9. [**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/)
10. **[Unsupervised Question Decomposition for Question Answering](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 222 citations*
11. **[Case-based Reasoning for Natural Language Queries over Knowledge Bases ↗](https://aclanthology.org/2021.emnlp-main.755/) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yet**Rajarshi Das et al.*2021 · 201 citations*
12. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
13. [**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/)
14. [**Studying Large Language Model Generalization with Influence Functions arXiv:2308.03296**Roger Grosse, Juhan Bae, Cem Anil, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/grosse-2023-studying-large-language-model-generalization/)
15. [**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/)
16. **[Training Language Models with Language Feedback at Scale ↗](https://arxiv.org/abs/2303.16755) arXiv:2303.16755 · no summary yet**Jérémy Scheurer et al.*2023 · 85 citations*
17. **[Alignment faking in large language models ↗](https://arxiv.org/abs/2412.14093) arXiv (Cornell University) · no summary yet**Ryan Greenblatt et al.*2024 · 82 citations*
18. **[Question Decomposition Improves the Faithfulness of Model-Generated Reasoning ↗](https://arxiv.org/abs/2307.11768) arXiv (Cornell University) · no summary yet**Ansh Radhakrishnan et al.*2023 · 74 citations*
19. **[Training Language Models with Language Feedback ↗](https://arxiv.org/abs/2204.14146) arXiv (Cornell University) · no summary yet**Jérémy Scheurer et al.*2022 · 58 citations*
20. **[Improving Code Generation by Training with Natural Language Feedback ↗](https://arxiv.org/abs/2303.16749) arXiv:2303.16749 · no summary yet**Angelica Chen et al.*2023 · 47 citations*
21. **[Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models ↗](https://arxiv.org/abs/2406.10162) arXiv (Cornell University) · no summary yet**Carson Denison et al.*2024 · 46 citations*
22. **[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*
23. **[Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety ↗](https://arxiv.org/abs/2507.11473) arXiv (Cornell University) · no summary yet**Tomek Korbak et al.*2025 · 35 citations*
24. **[Language Models Learn to Mislead Humans via RLHF ↗](https://arxiv.org/abs/2409.12822) arXiv (Cornell University) · no summary yet**Jiaxin Wen et al.*2024 · 33 citations*
25. **[Specific versus General Principles for Constitutional AI ↗](https://arxiv.org/abs/2310.13798) arXiv (Cornell University) · no summary yet**Sandipan Kundu et al.*2023 · 28 citations*
26. **[Rissanen Data Analysis: Examining Dataset Characteristics via Description Length ↗](https://arxiv.org/abs/2103.03872) arXiv (Cornell University) · no summary yet**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 22 citations*
27. **[Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions ↗](https://aclanthology.org/2022.lnls-1.3/) Proceedings of the First Workshop on Learning with Natural Language Supervision · no summary yet**Alicia Parrish et al.*2022 · 13 citations*
28. **[Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought ↗](https://arxiv.org/abs/2403.05518) arXiv (Cornell University) · no summary yet**James Chua et al.*2024 · 10 citations*
29. **[Sabotage Evaluations for Frontier Models ↗](https://arxiv.org/abs/2410.21514) arXiv (Cornell University) · no summary yet**Joe Benton et al.*2024 · 7 citations*
30. **[Forecasting Rare Language Model Behaviors ↗](https://arxiv.org/abs/2502.16797) arXiv (Cornell University) · no summary yet**Erik Jones et al.*2025 · 3 citations*
31. **[Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks ↗](https://arxiv.org/abs/2601.04603) arXiv (Cornell University) · no summary yet**Hoagy Cunningham et al.*2026 · 2 citations*
32. **[Unsupervised Elicitation of Language Models ↗](https://arxiv.org/abs/2506.10139) arXiv (Cornell University) · no summary yet**Jiaxin Wen et al.*2025 · 2 citations*
33. **[Quantifying Elicitation of Latent Capabilities in Language Models ↗](https://doi.org/10.52202/085713-5029) neural information processing systems · no summary yet**Elizabeth Donoway et al.*2025 · 1 citation*