---
title: "Who cites Roger Grosse — 902 papers of our library"
description: "902 papers of the Elamur Research library cite the works of Roger Grosse, found in their bibliographies · page 1 of 3 · 1–100; the most cited first."
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# Who cites Roger Grosse

964 citations of their works from 902 papers of our library (of the 149,439 whose bibliographies we read) · 254 with a page here, the most cited first · page 1 of 3 · 1–100 · [← Roger Grosse](https://md.research.elamur.ai/free/en/authors/roger-grosse/)

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1. [**GPT-4 Technical Report arXiv:2303.08774**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 13,036 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
2. [**Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks arXiv:1511.06434**Alec Radford, Luke Metz, Soumith Chintala*2015 · 6,654 citations*](https://md.research.elamur.ai/free/en/papers/radford-2015-unsupervised-representation-learning-deep-convolutional/)
3. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
4. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
5. [**ImageNet Classification with Deep Convolutional Neural Networks NeurIPS 2012**Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton*2012 · 2,801 citations*](https://md.research.elamur.ai/free/en/papers/krizhevsky-2012-imagenet-classification-deep-convolutional/)
6. [**Measuring Mathematical Problem Solving With the MATH Dataset arXiv:2103.03874**Dan Hendrycks, Collin Burns, Saurav Kadavath, et al.*2021 · 2,395 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/)
7. [**Deep Unsupervised Learning using Nonequilibrium Thermodynamics arXiv:1503.03585**Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, et al.*2015 · 1,761 citations*](https://md.research.elamur.ai/free/en/papers/sohl-dickstein-2015-deep-unsupervised-learning-using-nonequilibrium/)
8. [**The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables arXiv:1611.00712**Chris J. Maddison, Andriy Mnih, Yee Whye Teh*2016 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/maddison-2016-concrete-distribution-continuous-relaxation-discrete/)
9. [**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/)
10. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 992 citations*
11. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
12. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
13. **[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*
14. [**OpenAssistant Conversations — Democratizing Large Language Model Alignment arXiv:2304.07327**Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, et al.*2023 · 518 citations*](https://md.research.elamur.ai/free/en/papers/kopf-2023-openassistant-conversations-democratizing-large-language/)
15. **[Inference-Time Intervention: Eliciting Truthful Answers from a Language Model](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 492 citations*
16. [**In-context Learning and Induction Heads arXiv:2209.11895**Catherine Olsson, Nelson Elhage, Neel Nanda, et al.*2022 · 475 citations*](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/)
17. **[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*
18. [**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/)
19. [**Editing Models with Task Arithmetic arXiv:2212.04089**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
20. [**Representation Engineering: A Top-Down Approach to AI Transparency arXiv:2310.01405**Andy Zou, Long Phan, Sarah Chen, et al.*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/)
21. [**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/)
22. [**Representation Learning: A Review and New Perspectives arXiv:1206.5538**Yoshua Bengio, Aaron Courville, Pascal Vincent*2012 · 383 citations*](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/)
23. [**Empowering Biomedical Discovery with AI Agents arXiv:2404.02831**Shanghua Gao, Ada Fang, Yepeng Huang, et al.*2024 · 333 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-empowering-biomedical-discovery-ai-agents/)
24. [**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/)
25. **[JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models](https://md.research.elamur.ai/free/en/papers/chao-2024-jailbreakbench-open-robustness-benchmark-jailbreaking/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Patrick Chao, Edoardo Debenedetti, Alexander Robey, et al.*2024 · 277 citations*
26. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/)
27. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models arXiv:2306.16388**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 270 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
28. **[Automatic Text Scoring Using Neural Networks](https://md.research.elamur.ai/free/en/papers/alikaniotis-2016-automatic-text-scoring-neural-networks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dimitrios Alikaniotis, Helen Yannakoudakis, Marek Rei*2016 · 259 citations*
29. [**MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics arXiv:2109.00110**Kunhao Zheng, Jesse Michael Han, Stanislas Polu*2021 · 257 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2021-minif2f-cross-system-benchmark-formal/)
30. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
31. **[LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kaiyu Yang, Aidan M. Swope, Alex Gu, et al.*2023 · 253 citations*
32. [**XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models arXiv:2308.01263**Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.*2023 · 244 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
33. [**Generative Language Modeling for Automated Theorem Proving arXiv:2009.03393**Stanislas Polu, Ilya Sutskever*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/)
34. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection arXiv:2302.12173**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
35. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
36. [**Steering Language Models With Activation Engineering arXiv:2308.10248**Alexander Matt Turner, Lisa Thiergart, Gavin Leech, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/turner-2023-steering-language-models-activation-engineering/)
37. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
38. **[FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow](https://md.research.elamur.ai/free/en/papers/ma-2019-flowseq-non-autoregressive-conditional-sequence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xuezhe Ma, Chunting Zhou, Xian Li, et al.*2019 · 218 citations*
39. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
40. **[Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://md.research.elamur.ai/free/en/papers/li-2020-optimus-organizing-sentences-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chunyuan Li, Xiang Gao, Yuan Li, et al.*2020 · 208 citations*
41. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
42. [**Refusal in Language Models Is Mediated by a Single Direction arXiv:2406.11717**Andy Arditi, Oscar Obeso, Aaquib Syed, et al.*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/arditi-2024-refusal-language-models-mediated-single/)
43. **[ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://md.research.elamur.ai/free/en/papers/cheng-2025-elephant-measuring-understanding-social-sycophancy/) [Science](https://md.research.elamur.ai/free/en/venues/science/)**Myra Cheng, Sunny Yu, Cinoo Lee, et al.*2025 · 188 citations*
44. [**Eliciting Latent Predictions from Transformers with the Tuned Lens arXiv:2303.08112**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
45. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
46. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
47. **[LILA: A Unified Benchmark for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 179 citations*
48. [**LESS: Selecting Influential Data for Targeted Instruction Tuning arXiv:2402.04333**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 178 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/)
49. [**Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs arXiv:2210.12283**Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, et al.*2022 · 178 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-draft-sketch-prove-guiding-formal/)
50. [**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/)
51. [**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/)
52. [**The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets arXiv:2310.06824**Samuel Marks, Max Tegmark*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/marks-2023-geometry-truth-emergent-linear-structure/)
53. **[The Linear Representation Hypothesis and the Geometry of Large Language Models](https://md.research.elamur.ai/free/en/papers/park-2023-linear-representation-hypothesis-geometry-large/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Kiho Park, Yo Joong Choe, Victor Veitch*2023 · 165 citations*
54. [**Abstraction and analogy-making in artificial intelligence**Melanie Mitchell*2021 · 161 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2021-abstraction-analogy-making-artificial-intelligence/)
55. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
56. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
57. [**A Roadmap to Pluralistic Alignment arXiv:2402.05070**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
58. [**Exploring Length Generalization in Large Language Models arXiv:2207.04901**Cem Anil, Yuhuai Wu, Anders Andreassen, et al.*2022 · 139 citations*](https://md.research.elamur.ai/free/en/papers/anil-2022-exploring-length-generalization-large-language/)
59. [**SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents arXiv:2310.11667**Xuhui Zhou, Hao Zhu, Leena Mathur, et al.*2023 · 138 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-sotopia-interactive-evaluation-social-intelligence/)
60. [**Simple synthetic data reduces sycophancy in large language models arXiv:2308.03958**Jerry Wei, Da Huang, Yifeng Lu, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/)
61. [**Formal Mathematics Statement Curriculum Learning arXiv:2202.01344**Stanislas Polu, Jesse Michael Han, Kunhao Zheng, et al.*2022 · 133 citations*](https://md.research.elamur.ai/free/en/papers/polu-2022-formal-mathematics-statement-curriculum-learning/)
62. [**Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback arXiv:2305.10142**Yao Fu, Hao Peng, Tushar Khot, et al.*2023 · 132 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-improving-language-model-negotiation-self/)
63. **[Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup](https://md.research.elamur.ai/free/en/papers/gao-2021-scaling-deep-contrastive-learning-batch/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Luyu Gao, Yunyi Zhang, Jiawei Han, et al.*2021 · 131 citations*
64. **[Evaluating the Moral Beliefs Encoded in LLMs](https://md.research.elamur.ai/free/en/papers/scherrer-2023-evaluating-moral-beliefs-encoded-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nino Scherrer, Claudia Shi, Amir Feder, et al.*2023 · 129 citations*
65. [**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/)
66. [**Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training arXiv:2401.05566**Evan Hubinger, Carson Denison, Jesse Mu, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/hubinger-2024-sleeper-agents-training-deceptive-llms/)
67. [**Finding Neurons in a Haystack: Case Studies with Sparse Probing arXiv:2305.01610**Wes Gurnee, Neel Nanda, Matthew Pauly, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/gurnee-2023-finding-neurons-haystack-case-studies/)
68. [**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/)
69. **[A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models](https://md.research.elamur.ai/free/en/papers/ji-2016-latent-variable-recurrent-neural-network/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yangfeng Ji, Gholamreza Haffari, Jacob Eisenstein*2016 · 121 citations*
70. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)
71. [**Emergent Linear Representations in World Models of Self-Supervised Sequence Models arXiv:2309.00941**Neel Nanda, Andrew Lee, Martin Wattenberg*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-emergent-linear-representations-world-models/)
72. [**Fundamental Limitations of Alignment in Large Language Models arXiv:2304.11082**Yotam Wolf, Noam Wies, Oshri Avnery, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2023-fundamental-limitations-alignment-large-language/)
73. [**DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data arXiv:2405.14333**Huajian Xin, Daya Guo, Zhihong Shao, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-advancing-theorem-proving/)
74. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
75. [**Inverse Scaling: When Bigger Isn't Better arXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
76. [**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/)
77. [**Model evaluation for extreme risks arXiv:2305.15324**Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/shevlane-2023-model-evaluation-extreme-risks/)
78. [**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/)
79. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 99 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
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83. [**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/)
84. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/)
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88. [**Proof Artifact Co-training for Theorem Proving with Language Models arXiv:2102.06203**Jesse Michael Han, Jason Rute, Yuhuai Wu, et al.*2021 · 82 citations*](https://md.research.elamur.ai/free/en/papers/han-2021-proof-artifact-co-training-theorem/)
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98. [**ARB: Advanced Reasoning Benchmark for Large Language Models arXiv:2307.13692**Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/sawada-2023-arb-advanced-reasoning-benchmark-large/)
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