---
title: "Who cites Eric J. Michaud — 68 papers of our library"
description: "68 papers of the Elamur Research library cite the works of Eric J. Michaud, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/eric-j-michaud/cited-by/"
---
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# Who cites Eric J. Michaud

79 citations of their works from 68 papers of our library (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← Eric J. Michaud](https://md.research.elamur.ai/free/en/authors/eric-j-michaud/)

1. **[Are Emergent Abilities of Large Language Models a Mirage?](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rylan Schaeffer, Brando Miranda, Sanmi Koyejo*2023 · 342 citations*
2. **[Faith and Fate: Limits of Transformers on Compositionality](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 327 citations*
3. [**Progress measures for grokking via mechanistic interpretability arXiv:2301.05217**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 228 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/)
4. **[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*
5. [**RAFT: Adapting Language Model to Domain Specific RAG arXiv:2403.10131**Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/)
6. [**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/)
7. [**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/)
8. **[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*
9. [**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/)
10. **[Towards Understanding Grokking: An Effective Theory of Representation Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-towards-understanding-grokking-effective-theory/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ziming Liu, Ouail Kitouni, Niklas Nolte, et al.*2022 · 65 citations*
11. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
12. [**Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance arXiv:2403.16952**Jiasheng Ye, Peiju Liu, Tianxiang Sun, et al.*2024 · 52 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-data-mixing-laws-optimizing-data/)
13. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations*
14. [**Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization arXiv:2405.15071**Boshi Wang, Xiang Yue, Yu Su, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-grokked-transformers-implicit-reasoners-mechanistic/)
15. [**Omnigrok: Grokking Beyond Algorithmic Data arXiv:2210.01117**Ziming Liu, Eric J. Michaud, Max Tegmark*2022 · 29 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-omnigrok-grokking-algorithmic-data/)
16. [**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/)
17. [**Language Models Use Trigonometry to Do Addition arXiv:2502.00873**Subhash Kantamneni, Max Tegmark*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/kantamneni-2025-language-models-use-trigonometry-addition/)
18. [**Chinchilla Scaling: A replication attempt arXiv:2404.10102**Tamay Besiroglu, Ege Erdil, Matthew Barnett, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/besiroglu-2024-chinchilla-scaling-replication-attempt/)
19. **[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*
20. [**Towards Understanding Distilled Reasoning Models: A Representational Approach arXiv:2503.03730 · ICLR 2025 Workshop on Building Trust in Language Models and Applications**David D. Baek, Max Tegmark*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/baek-2025-towards-understanding-distilled-reasoning-models/)
21. **[The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation Analysis](https://md.research.elamur.ai/free/en/papers/mueller-2026-quest-right-mediator-surveying-mechanistic/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Aaron Mueller, Jannik Brinkmann, Millicent Li, et al.*2026 · 7 citations*
22. **[Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning](https://md.research.elamur.ai/free/en/papers/zhai-2026-maximizing-local-entropy-where-it/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Naixin Zhai, Pengyang Shao, Binbin Zheng, et al.*2026 · 2 citations*
23. **[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*
24. **[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*
25. **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*

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

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