---
title: "Tatsunori Hashimoto — all works — Elamur Research"
description: "All 69 works of Tatsunori Hashimoto in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
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---
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# Tatsunori Hashimoto — all works

69 works · [← Tatsunori Hashimoto](https://md.research.elamur.ai/free/en/authors/tatsunori-hashimoto/)

1. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
2. **[Holistic Evaluation of Language Models ↗](https://arxiv.org/abs/2211.09110) arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023 · no summary yet**Percy Liang et al.*2022 · 1,001 citations*
3. **[Benchmarking Large Language Models for News Summarization](https://md.research.elamur.ai/free/en/papers/zhang-2024-benchmarking-large-language-models-news/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tianyi Zhang, Faisal Ladhak, Esin Durmus, et al.*2024 · 671 citations*
4. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations*
5. **[AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 456 citations*
6. [**Diffusion-LM Improves Controllable Text Generation arXiv:2205.14217**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 440 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
7. **[Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization ↗](https://arxiv.org/abs/1911.08731) arXiv (Cornell University) · no summary yet**Shiori Sagawa et al.*2019 · 430 citations*
8. **[Generating Sentences by Editing Prototypes](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, et al.*2017 · 425 citations*
9. [**Whose Opinions Do Language Models Reflect? arXiv:2303.17548**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 414 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
10. **[Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale ↗](https://doi.org/10.1145/3593013.3594095) ACM Conference on Fairness, Accountability, and Transparency (FAccT) · no summary yet**Federico Bianchi et al.*2023 · 377 citations*
11. **[Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators ↗](https://arxiv.org/abs/2404.04475) arXiv:2404.04475 · no summary yet**Yann Dubois et al.*2024 · 310 citations*
12. [**Contrastive Decoding: Open-ended Text Generation as Optimization arXiv:2210.15097**Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/)
13. **[Fairness Without Demographics in Repeated Loss Minimization ↗](https://arxiv.org/abs/1806.08010) arXiv (Cornell University) · no summary yet**Tatsunori Hashimoto et al.*2018 · 254 citations*
14. **[Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks ↗](https://doi.org/10.1109/spw63631.2024.00018) IEEE Security and Privacy Workshops (SPW) · no summary yet**Daniel D. Kang et al.*2024 · 206 citations*
15. [**Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers arXiv:2409.04109**Chenglei Si, Diyi Yang, Tatsunori Hashimoto*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/)
16. **[Large Language Models Can Be Strong Differentially Private Learners ↗](https://arxiv.org/abs/2110.05679) arXiv (Cornell University) · no summary yet**Xuechen Li et al.*2021 · 166 citations*
17. **[A Retrieve-and-Edit Framework for Predicting Structured Outputs ↗](https://arxiv.org/abs/1812.01194) arXiv (Cornell University) · no summary yet**Tatsunori Hashimoto et al.*2018 · 164 citations*
18. **[Distributionally Robust Neural Networks ↗](https://openalex.org/W2995793065) International Conference on Learning Representations · no summary yet**Shiori Sagawa et al.*2020 · 161 citations*
19. **[Distributionally Robust Language Modeling ↗](https://aclanthology.org/D19-1432/) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yet**Yonatan Oren et al.*2019 · 149 citations*
20. [**Unifying Human and Statistical Evaluation for Natural Language Generation arXiv:1904.02792**Tatsunori B. Hashimoto, Hugh Zhang, Percy Liang*2019 · 143 citations*](https://md.research.elamur.ai/free/en/papers/hashimoto-2019-unifying-human-statistical-evaluation-natural/)
21. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions arXiv:2309.07875**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
22. **[Foundation Models and Fair Use ↗](https://doi.org/10.2139/ssrn.4404340) SSRN Electronic Journal · no summary yet**Peter B. Henderson et al.*2023 · 134 citations*
23. **[Improved Natural Language Generation via Loss Truncation](https://md.research.elamur.ai/free/en/papers/kang-2020-improved-natural-language-generation-loss/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Kang, Tatsunori B. Hashimoto*2020 · 130 citations*
24. **[Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models ↗](https://aclanthology.org/2023.emnlp-main.335/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Kaitlyn Zhou, Dan Jurafsky, Tatsunori Hashimoto*2023 · 123 citations*
25. **[Contrastive Decoding: Open-ended Text Generation as Optimization ↗](https://aclanthology.org/2023.acl-long.687/) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Xiang Lisa Li et al.*2023 · 113 citations*
26. [**Robust Distortion-free Watermarks for Language Models arXiv:2307.15593**Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/kuditipudi-2023-robust-distortion-free-watermarks-language/)
27. [**Identifying the Risks of LM Agents with an LM-Emulated Sandbox arXiv:2309.15817**Yangjun Ruan, Honghua Dong, Andrew Wang, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2023-identifying-risks-lm-agents-lm/)
28. [**Proving Test Set Contamination in Black Box Language Models arXiv:2310.17623**Yonatan Oren, Nicole Meister, Niladri Chatterji, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/oren-2023-proving-test-set-contamination-black/)
29. **[Identifying and Mitigating the Security Risks of Generative AI ↗](https://doi.org/10.1561/3300000041) Foundations and Trends® in Privacy and Security · no summary yet**Clark Barrett et al.*2023 · 79 citations*
30. **[When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization ↗](https://aclanthology.org/2023.eacl-main.234/) Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · no summary yet**Faisal Ladhak et al.*2023 · 75 citations*
31. **[Coder Reviewer Reranking for Code Generation ↗](https://arxiv.org/abs/2211.16490) arXiv:2211.16490 · no summary yet**Tianyi Zhang et al.*2022 · 52 citations*
32. **[Robustness to Spurious Correlations via Human Annotations ↗](https://arxiv.org/abs/2007.06661) arXiv (Cornell University) · no summary yet**Megha Srivastava, Tatsunori Hashimoto, Percy Liang*2020 · 47 citations*
33. **[MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks](https://md.research.elamur.ai/free/en/papers/nie-2023-moca-measuring-human-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Allen Nie, Yuhui Zhang, Atharva Amdekar, et al.*2023 · 41 citations*
34. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
35. **[Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions ↗](https://arxiv.org/abs/2106.03873) arXiv:2106.03873 · no summary yet**Dorottya Demszky et al.*2021 · 40 citations*
36. **[Benchmarking Distributional Alignment of Large Language Models](https://md.research.elamur.ai/free/en/papers/meister-2025-benchmarking-distributional-alignment-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nicole Meister, Carlos Guestrin, Tatsunori Hashimoto*2025 · 35 citations*
37. **[On the Learnability of Watermarks for Language Models ↗](https://arxiv.org/abs/2312.04469) arXiv (Cornell University) · no summary yet**Chenchen Gu et al.*2023 · 28 citations*
38. **[Benchmarking and Improving Generator-Validator Consistency of Language Models ↗](https://arxiv.org/abs/2310.01846) arXiv (Cornell University) · no summary yet**Xiang Lisa Li et al.*2023 · 27 citations*
39. **[Scaling up Trustless DNN Inference with Zero-Knowledge Proofs ↗](https://arxiv.org/abs/2210.08674) arXiv (Cornell University) · no summary yet**Daniel D. Kang et al.*2022 · 27 citations*
40. **[DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference ↗](https://aclanthology.org/2021.naacl-main.88/) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yet**Shikhar Murty, Tatsunori B. Hashimoto, Christopher Manning*2021 · 27 citations*
41. **[Improving Pretraining Data Using Perplexity Correlations ↗](https://arxiv.org/abs/2409.05816) arXiv (Cornell University) · no summary yet**Tristan Thrush, Christopher E. Potts, Tatsunori Hashimoto*2024 · 24 citations*
42. **[AutoBencher: Towards Declarative Benchmark Construction ↗](https://arxiv.org/abs/2407.08351) arXiv (Cornell University) · no summary yet**Xiang Lisa Li et al.*2024 · 22 citations*
43. **[One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention ↗](https://arxiv.org/abs/2307.03576) arXiv:2307.03576 · no summary yet**Arvind Mahankali, Tatsunori B. Hashimoto, Tengyu Ma*2023 · 22 citations*
44. **[The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas ↗](https://arxiv.org/abs/2506.20803) arXiv:2506.20803 · no summary yet**Chenglei Si, Tatsunori Hashimoto, Diyi Yang*2025 · 19 citations*
45. **[Extending the WILDS Benchmark for Unsupervised Adaptation ↗](https://arxiv.org/abs/2112.05090) arXiv (Cornell University) · no summary yet**Shiori Sagawa et al.*2021 · 18 citations*
46. **[Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning ↗](https://arxiv.org/abs/2207.07635) arXiv (Cornell University) · no summary yet**Shibani Santurkar et al.*2022 · 15 citations*
47. **[Improving Self-Supervised Learning by Characterizing Idealized Representations ↗](https://doi.org/10.52202/068431-0820) neural information processing systems · no summary yet**Yann Dubois et al.*2022 · 11 citations*
48. **[End-to-End Test-Time Training for Long Context ↗](https://arxiv.org/abs/2512.23675) arXiv (Cornell University) · no summary yet**Arnuv Tandon et al.*2025 · 10 citations*
49. **[Eliciting Language Model Behaviors with Investigator Agents ↗](https://arxiv.org/abs/2502.01236) arXiv (Cornell University) · no summary yet**Xiang Lisa Li et al.*2025 · 8 citations*
50. **[Modeling the Second Player in Distributionally Robust Optimization ↗](https://arxiv.org/abs/2103.10282) arXiv (Cornell University) · no summary yet**Paul Michel, Tatsunori Hashimoto, Graham Neubig*2021 · 8 citations*
51. **[Learning Autocomplete Systems as a Communication Game ↗](https://arxiv.org/abs/1911.06964) arXiv (Cornell University) · no summary yet**Mina Lee, Tatsunori Hashimoto, Percy Liang*2019 · 8 citations*
52. **[Unsupervised Transformation Learning via Convex Relaxations ↗](https://arxiv.org/abs/1711.02226) arXiv (Cornell University) · no summary yet**Tatsunori Hashimoto, John C. Duchi, Percy Liang*2017 · 8 citations*
53. [**Towards Execution-Grounded Automated AI Research arXiv:2601.14525**Chenglei Si, Zitong Yang, Yejin Choi, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/si-2026-towards-execution-grounded-automated-ai/)
54. **[Pre-training under infinite compute ↗](https://arxiv.org/abs/2509.14786) arXiv (Cornell University) · no summary yet**Kim, Konwoo et al.*2025 · 7 citations*
55. **[Inferring Multidimensional Rates of Aging from Cross-Sectional Data ↗](https://arxiv.org/abs/1807.04709) PubMed · no summary yet**Emma Pierson et al.*2018 · 7 citations*
56. **[Learning to (Learn at Test Time) ↗](https://arxiv.org/abs/2310.13807) arXiv:2310.13807 · no summary yet**Yu Sun et al.*2023 · 6 citations*
57. **[FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users ↗](https://arxiv.org/abs/2502.19312) arXiv (Cornell University) · no summary yet**Anikait Singh et al.*2025 · 5 citations*
58. **[Auditing Prompt Caching in Language Model APIs ↗](https://arxiv.org/abs/2502.07776) arXiv (Cornell University) · no summary yet**Gu, Chenchen et al.*2025 · 4 citations*
59. **[ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation ↗](https://arxiv.org/abs/2211.04775) arXiv (Cornell University) · no summary yet**Daniel D. Kang et al.*2022 · 4 citations*
60. **[Evaluating Self-Supervised Learning via Risk Decomposition ↗](https://arxiv.org/abs/2302.03068) arXiv (Cornell University) · no summary yet**Dubois, Yann, Tatsunori Hashimoto, Percy Liang*2023 · 3 citations*
61. **[Trustless Audits without Revealing Data or Models ↗](https://arxiv.org/abs/2404.04500) arXiv (Cornell University) · no summary yet**Suppakit Waiwitlikhit et al.*2024 · 2 citations*
62. **[Distributionally Robust Models with Parametric Likelihood Ratios ↗](https://arxiv.org/abs/2204.06340) arXiv (Cornell University) · no summary yet**Paul Michel, Tatsunori Hashimoto, Graham Neubig*2022 · 2 citations*
63. **[Data-efficient pre-training by scaling synthetic megadocs ↗](https://arxiv.org/abs/2603.18534) arXiv (Cornell University) · no summary yet**Konwoo Kim et al.*2026 · 1 citation*
64. **[Out-of-Domain Robustness via Targeted Augmentations ↗](https://arxiv.org/abs/2302.11861) arXiv (Cornell University) · no summary yet**Irena Gao et al.*2023 · 1 citation*
65. **[Trustworthy oversight of machine learning via randomization ↗](https://doi.org/10.25740/ys987nd5770) Stanford Digital Repository · no summary yet**Rohith Kuditipudi et al.*2026*
66. **[Synthetic Data for any Differentiable Target ↗](https://arxiv.org/abs/2604.08423) arXiv (Cornell University) · no summary yet**Tristan Thrush et al.*2026*
67. **[Taming and leveraging inconsistencies in pretrained language models ↗](https://doi.org/10.25740/mn037bj2590) Stanford Digital Repository · no summary yet**Tianyi Zhang et al.*2026*
68. **[Will Scaling Improve Social Simulation with LLMs? ↗](https://arxiv.org/abs/2607.02464) arXiv (Cornell University) · no summary yet**Caleb Ziems et al.*2026*
69. **[CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition ↗](https://arxiv.org/abs/2502.01777) arXiv (Cornell University) · no summary yet**Martijn Bartelds et al.*2025*