---
title: "Who cites Lawrence Chan — 230 papers of our library"
description: "230 papers of the Elamur Research library cite the works of Lawrence Chan, found in their bibliographies; the most cited first."
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---
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# 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](https://md.research.elamur.ai/free/en/authors/lawrence-chan/)

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. **[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*
3. [**Scaling Laws for Reward Model Overoptimization arXiv:2210.10760**Leo Gao, John Schulman, Jacob Hilton*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-scaling-laws-reward-model-overoptimization/)
4. [**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/)
5. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-do-llamas-work-english-latent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 177 citations*
6. [**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/)
7. [**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/)
8. **[Are aligned neural networks adversarially aligned?](https://md.research.elamur.ai/free/en/papers/carlini-2023-aligned-neural-networks-adversarially-aligned/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**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 model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 151 citations*
10. [**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/)
11. [**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/)
12. [**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/)
13. [**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/)
14. [**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/)
15. [**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/)
16. [**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/)
17. [**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/)
18. [**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/)
19. [**Towards Best Practices of Activation Patching in Language Models: Metrics and Methods arXiv:2309.16042**Fred Zhang, Neel Nanda*2023 · 97 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-towards-best-practices-activation-patching/)
20. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-llamas-work-english-latent-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 96 citations*
21. **[A Survey of Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Timo Kaufmann, Paul Weng, Viktor Bengs, et al.*2023 · 92 citations*
22. **[Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://md.research.elamur.ai/free/en/papers/rame-2023-rewarded-soups-towards-pareto-optimal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.*2023 · 90 citations*
23. **[Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://md.research.elamur.ai/free/en/papers/wang-2025-reinforcement-learning-reasoning-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.*2025 · 87 citations*
24. [**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/)
25. **[Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*
26. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
27. [**What Algorithms can Transformers Learn? A Study in Length Generalization arXiv:2310.16028**Hattie Zhou, Arwen Bradley, Etai Littwin, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/)
28. [**Linear Representations of Sentiment in Large Language Models arXiv:2310.15154**Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/tigges-2023-linear-representations-sentiment-large-language/)
29. [**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/)
30. **[Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries](https://md.research.elamur.ai/free/en/papers/biran-2024-hopping-too-late-exploring-limitations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eden Biran, Daniela Gottesman, Sohee Yang, et al.*2024 · 55 citations*
31. [**ARGS: Alignment as Reward-Guided Search arXiv:2402.01694**Maxim Khanov, Jirayu Burapacheep, Yixuan Li*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/khanov-2024-args-alignment-reward-guided-search/)
32. **[Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-lost-multilinguality-dissecting-cross-lingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingyang Wang, Heike Adel, Lukas Lange, et al.*2025 · 31 citations*
33. [**Pretraining on the Test Set Is All You Need arXiv:2309.08632**Rylan Schaeffer*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-pretraining-test-set-all-need/)
34. **[Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals](https://md.research.elamur.ai/free/en/papers/ortu-2024-competition-mechanisms-tracing-how-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Francesco Ortu, Zhijing Jin, Diego Doimo, et al.*2024 · 23 citations*
35. [**Language Models as Models of Language arXiv:2408.07144**Raphael Milliere*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/milliere-2024-language-models-models-language/)
36. **[A Survey of Post-Training Scaling in Large Language Models](https://md.research.elamur.ai/free/en/papers/lai-2025-survey-post-training-scaling-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanyu Lai, Xiao Liu, Junjie Gao, et al.*2025 · 18 citations*
37. **[When One LLM Drools, Multi-LLM Collaboration Rules](https://md.research.elamur.ai/free/en/papers/feng-2026-when-one-llm-drools-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shangbin Feng, Wenxuan Ding, Alisa Liu, et al.*2026 · 14 citations*
38. **[Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race](https://md.research.elamur.ai/free/en/papers/sun-2025-aligned-but-blind-alignment-increases/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
40. **[Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models](https://md.research.elamur.ai/free/en/papers/mondorf-2025-circuit-compositions-exploring-modular-structures/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Philipp Mondorf, Sondre Wold, Barbara Plank*2025 · 8 citations*
41. **[All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens](https://md.research.elamur.ai/free/en/papers/mamidanna-2025-all-one-llms-solve-mental/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Verbalization](https://md.research.elamur.ai/free/en/papers/zaman-2026-is-chain-thought-really-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kerem Zaman, Shashank Srivastava*2026 · 5 citations*
43. **[When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/ortu-2026-when-seeing-overrides-knowing-disentangling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Activation](https://md.research.elamur.ai/free/en/papers/yang-2026-how-chain-thought-works-tracing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Yang, Qinghua Zhao, Lei Li, et al.*2026 · 1 citation*
45. **[Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting Circuits](https://md.research.elamur.ai/free/en/papers/zhao-2026-do-transformers-grok-succinct-algorithms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizheng Zhao*2026*
46. **[GeoLAN: Geometric Learning of Latent Explanatory Directions in Large Language Models](https://md.research.elamur.ai/free/en/papers/pan-2026-geolan-geometric-learning-latent-explanatory/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tianyu Pan, Damon L. Woodard*2026*
47. **[Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization](https://md.research.elamur.ai/free/en/papers/zhao-2026-hallucinations-as-orthogonal-noise-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingkuan Zhao, Wentao Hu, Tianchen Huang, et al.*2026*
48. **[How Do LLMs Generate Contrastive Sentiments? A Mechanistic Perspective](https://md.research.elamur.ai/free/en/papers/nguyen-2026-how-do-llms-generate-contrastive/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Van Bach Nguyen, Jörg Schlötterer, Christin Seifert*2026*
49. **[Language Models Learn Universal Representations of Numbers and Here's Why You Should Care](https://md.research.elamur.ai/free/en/papers/stefanik-2026-language-models-learn-universal-representations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michal Štefánik, Timothee Mickus, Marek Kadlčík, et al.*2026*
50. **[Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States](https://md.research.elamur.ai/free/en/papers/sahoo-2026-linear-probes-detect-task-format/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Subramanyam Sahoo, Vinija Jain, Aman Chadha, et al.*2026*
51. **[Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning](https://md.research.elamur.ai/free/en/papers/yamauchi-2026-mapping-circumplex-affect-geometric-analysis/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yusuke Yamauchi, Akiko Aizawa*2026*
52. **[Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs](https://md.research.elamur.ai/free/en/papers/zhu-2026-memorization-emergence-explaining-reversal-failures/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihua Zhu, Qianying Liu, Jiaxin Wang, et al.*2026*
53. **[Memory Dial: A Training Framework for Controllable Memorization in Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-memory-dial-training-framework-controllable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangbo Zhang, Ali Emami*2026*
54. **[Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding](https://md.research.elamur.ai/free/en/papers/storek-2026-sense-sensitivity-examining-influence-semantic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Adam Štorek, Mukur Gupta, Samira Hajizadeh, et al.*2026*

176 more citing papers of our library have no page here yet.

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