🧑‍🔬 Who cites Lawrence Chan

237 citations of their works from 230 papers of our library (of the 149,439 whose bibliographies we read) · 54 with a page here, the most cited first · ← Lawrence Chan

1. GPT-4 Technical ReportarXiv:2303.08774OpenAI, Josh Achiam, Steven Adler, et al.2023 · 13,036 citations
2. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
3. Scaling Laws for Reward Model OveroptimizationarXiv:2210.10760Leo Gao, John Schulman, Jacob Hilton2022 · 323 citations
4. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
5. Do Llamas Work in English? On the Latent Language of Multilingual TransformersAnnual Meeting of the Association for Computational Linguistics (ACL)Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.2024 · 177 citations
6. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
7. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
8. Are aligned neural networks adversarially aligned?Conference on Neural Information Processing Systems (NeurIPS)Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, et al.2023 · 157 citations
9. How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language modelConference on Neural Information Processing Systems (NeurIPS)Michael Hanna, Ollie Liu, Alexandre Variengien2023 · 151 citations
10. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
11. Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingarXiv:2401.05566Evan Hubinger, Carson Denison, Jesse Mu, et al.2024 · 123 citations
12. Finding Neurons in a Haystack: Case Studies with Sparse ProbingarXiv:2305.01610Wes Gurnee, Neel Nanda, Matthew Pauly, et al.2023 · 123 citations
13. Emergent Linear Representations in World Models of Self-Supervised Sequence ModelsarXiv:2309.00941Neel Nanda, Andrew Lee, Martin Wattenberg2023 · 116 citations
14. Fundamental Limitations of Alignment in Large Language ModelsarXiv:2304.11082Yotam Wolf, Noam Wies, Oshri Avnery, et al.2023 · 114 citations
15. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
16. Inverse Scaling: When Bigger Isn't BetterarXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQFIan R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.2023 · 105 citations
17. Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations
18. Model evaluation for extreme risksarXiv:2305.15324Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, et al.2023 · 104 citations
19. Towards Best Practices of Activation Patching in Language Models: Metrics and MethodsarXiv:2309.16042Fred Zhang, Neel Nanda2023 · 97 citations
20. Do Llamas Work in English? On the Latent Language of Multilingual TransformersAnnual Meeting of the Association for Computational Linguistics (ACL)Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.2024 · 96 citations
21. A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
22. Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsConference on Neural Information Processing Systems (NeurIPS)Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.2023 · 90 citations
23. Reinforcement Learning for Reasoning in Large Language Models with One Training ExampleConference on Neural Information Processing Systems (NeurIPS)Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.2025 · 87 citations
24. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
25. Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language GenerationTransactions of the Association for Computational Linguistics (TACL)Patrick Fernandes, Aman Madaan, Emmy Liu, et al.2023 · 84 citations
26. A Primer on the Inner Workings of Transformer-based Language ModelsarXiv:2405.00208Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.2024 · 72 citations
27. What Algorithms can Transformers Learn? A Study in Length GeneralizationarXiv:2310.16028Hattie Zhou, Arwen Bradley, Etai Littwin, et al.2023 · 67 citations
28. Linear Representations of Sentiment in Large Language ModelsarXiv:2310.15154Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, et al.2023 · 64 citations
29. Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in ChinchillaarXiv:2307.09458Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.2023 · 58 citations
30. Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop QueriesConference on Empirical Methods in Natural Language Processing (EMNLP)Eden Biran, Daniela Gottesman, Sohee Yang, et al.2024 · 55 citations
31. ARGS: Alignment as Reward-Guided SearcharXiv:2402.01694Maxim Khanov, Jirayu Burapacheep, Yixuan Li2024 · 46 citations
32. Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mingyang Wang, Heike Adel, Lukas Lange, et al.2025 · 31 citations
33. Pretraining on the Test Set Is All You NeedarXiv:2309.08632Rylan Schaeffer2023 · 24 citations
34. Competition of Mechanisms: Tracing How Language Models Handle Facts and CounterfactualsAnnual Meeting of the Association for Computational Linguistics (ACL)Francesco Ortu, Zhijing Jin, Diego Doimo, et al.2024 · 23 citations
35. Language Models as Models of LanguagearXiv:2408.07144Raphael Milliere2024 · 21 citations
36. A Survey of Post-Training Scaling in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hanyu Lai, Xiao Liu, Junjie Gao, et al.2025 · 18 citations
37. When One LLM Drools, Multi-LLM Collaboration RulesAnnual Meeting of the Association for Computational Linguistics (ACL)Shangbin Feng, Wenxuan Ding, Alisa Liu, et al.2026 · 14 citations
38. Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of RaceAnnual Meeting of the Association for Computational Linguistics (ACL)Lihao Sun, Chengzhi Mao, Valentin Hofmann, et al.2025 · 10 citations
39. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language ModelsFindings of the Association for Computational LinguisticsHengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.2026 · 9 citations
40. Circuit Compositions: Exploring Modular Structures in Transformer-Based Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Philipp Mondorf, Sondre Wold, Barbara Plank2025 · 8 citations
41. All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other TokensConference on Empirical Methods in Natural Language Processing (EMNLP)Siddarth Mamidanna, Daking Rai, Ziyu Yao, et al.2025 · 6 citations
42. Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint VerbalizationAnnual Meeting of the Association for Computational Linguistics (ACL)Kerem Zaman, Shashank Srivastava2026 · 5 citations
43. When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Francesco Ortu, Zhijing Jin, Diego Doimo, et al.2026 · 2 citations
44. How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and ActivationFindings of the Association for Computational LinguisticsHao Yang, Qinghua Zhao, Lei Li, et al.2026 · 1 citation
45. Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting CircuitsFindings of the Association for Computational LinguisticsYizheng Zhao2026
46. GeoLAN: Geometric Learning of Latent Explanatory Directions in Large Language ModelsFindings of the Association for Computational LinguisticsTianyu Pan, Damon L. Woodard2026
47. Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual OrthogonalizationFindings of the Association for Computational LinguisticsMingkuan Zhao, Wentao Hu, Tianchen Huang, et al.2026
48. How Do LLMs Generate Contrastive Sentiments? A Mechanistic PerspectiveConference of the European Chapter of the Association for Computational Linguistics (EACL)Van Bach Nguyen, Jörg Schlötterer, Christin Seifert2026
49. Language Models Learn Universal Representations of Numbers and Here's Why You Should CareAnnual Meeting of the Association for Computational Linguistics (ACL)Michal Štefánik, Timothee Mickus, Marek Kadlčík, et al.2026
50. Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden StatesWorkshop on Trustworthy NLPSubramanyam Sahoo, Vinija Jain, Aman Chadha, et al.2026
51. Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yusuke Yamauchi, Akiko Aizawa2026
52. Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yihua Zhu, Qianying Liu, Jiaxin Wang, et al.2026
53. Memory Dial: A Training Framework for Controllable Memorization in Language ModelsFindings of the Association for Computational LinguisticsXiangbo Zhang, Ali Emami2026
54. Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code UnderstandingAnnual Meeting of the Association for Computational Linguistics (ACL)Adam Štorek, Mukur Gupta, Samira Hajizadeh, et al.2026

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