🔗 Who cites «In-context Learning and Induction Heads»

391 papers of our library cite it (of the 149,439 whose bibliographies we read) · 74 with a page here, the most cited first · ← back to «In-context Learning and Induction Heads»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. Sparks of Artificial General Intelligence: Early experiments with GPT-4arXiv:2303.12712Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.2023 · 2,439 citations
3. Emergent Abilities of Large Language ModelsarXiv:2206.07682Jason Wei, Yi Tay, Rishi Bommasani, et al.2022 · 2,269 citations
4. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
5. Explainability for Large Language Models: A SurveyACM Transactions on Intelligent Systems and TechnologyHaiyan Zhao, Hanjie Chen, Fan Yang, et al.2023 · 790 citations
6. STaR: Bootstrapping Reasoning With ReasoningEric Zelikman, Yuhuai Wu, Jesse Mu, et al.2022 · 777 citations
7. RULER: What's the Real Context Size of Your Long-Context Language Models?arXiv:2404.06654Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.2024 · 400 citations
8. Representation Engineering: A Top-Down Approach to AI TransparencyarXiv:2310.01405Andy Zou, Long Phan, Sarah Chen, et al.2023 · 397 citations
9. The AI Scientist: Towards Fully Automated Open-Ended Scientific DiscoveryarXiv:2408.06292Chris Lu, Cong Lu, Robert Tjarko Lange, et al.2024 · 372 citations
10. SnapKV: LLM Knows What You are Looking for Before GenerationConference on Neural Information Processing Systems (NeurIPS)Yuhong Li, Yingbing Huang, Bowen Yang, et al.2024 · 355 citations
11. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
12. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-OptimizersFindings of the Association for Computational LinguisticsDamai Dai, Yutao Sun, Li Dong, et al.2023 · 257 citations
13. Transformers learn in-context by gradient descentarXiv:2212.07677Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.2022 · 247 citations
14. What learning algorithm is in-context learning? Investigations with linear modelsarXiv:2211.15661Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.2022 · 236 citations
15. Steering Language Models With Activation EngineeringarXiv:2308.10248Alexander Matt Turner, Lisa Thiergart, Gavin Leech, et al.2023 · 233 citations
16. Progress measures for grokking via mechanistic interpretabilityarXiv:2301.05217Neel Nanda, Lawrence Chan, Tom Lieberum, et al.2023 · 228 citations
17. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
18. Jamba: A Hybrid Transformer-Mamba Language ModelarXiv:2403.19887Opher Lieber, Barak Lenz, Hofit Bata, et al.2024 · 159 citations
19. In-Context Learning Creates Task VectorsFindings of the Association for Computational LinguisticsRoee Hendel, Mor Geva, Amir Globerson2023 · 159 citations
20. Language Model Behavior: A Comprehensive SurveyComputational LinguisticsTyler A. Chang, Benjamin K. Bergen2024 · 148 citations
21. Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 142 citations
22. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
23. 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
24. What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task LearningFindings of the Association for Computational LinguisticsJane Pan, Tianyu Gao, Howard Chen, et al.2023 · 102 citations
25. Retrieval Head Mechanistically Explains Long-Context FactualityarXiv:2404.15574Wenhao Wu, Yizhong Wang, Guangxuan Xiao, et al.2024 · 94 citations
26. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
27. Interpretability at Scale: Identifying Causal Mechanisms in AlpacaConference on Neural Information Processing Systems (NeurIPS)Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.2023 · 81 citations
28. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
29. Simple linear attention language models balance the recall-throughput tradeoffarXiv:2402.18668Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.2024 · 59 citations
30. Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in ChinchillaarXiv:2307.09458Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.2023 · 58 citations
31. Zoology: Measuring and Improving Recall in Efficient Language ModelsarXiv:2312.04927Simran Arora; Sabri Eyuboglu; Aman Timalsina; Isys Johnson; Michael Poli; James Zou; Atri Rudra;, Christopher Re2023 · 55 citations
32. The Learnability of In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Noam Wies, Yoav Levine, Amnon Shashua2023 · 53 citations
33. The MiniPile Challenge for Data-Efficient Language ModelsarXiv:2304.08442Jean Kaddour2023 · 41 citations
34. Through the Lens of Core Competency: Survey on Evaluation of Large Language ModelsarXiv:2308.07902Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.2023 · 35 citations
35. Rigorously Assessing Natural Language Explanations of NeuronsBlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLPJing Huang, Atticus Geiger, Karel D'Oosterlinck, et al.2023 · 30 citations
36. The Quantization Model of Neural ScalingarXiv:2303.13506Eric J. Michaud, Ziming Liu, Uzay Girit, et al.2023 · 30 citations
37. RWKV-7 "Goose" with Expressive Dynamic State EvolutionarXiv:2503.14456Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.2025 · 23 citations
38. 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
39. TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence ConfigurationConference on Empirical Methods in Natural Language Processing (EMNLP)Yanshu Li, Jianjiang Yang, Tian Yun, et al.2025 · 22 citations
40. Personality Alignment of Large Language ModelsarXiv:2408.11779Minjun Zhu, Yixuan Weng, Linyi Yang, et al.2024 · 21 citations
41. Understanding Emergent In-Context Learning from a Kernel Regression PerspectivearXiv:2305.12766Chi Han, Ziqi Wang, Han Zhao, et al.2023 · 21 citations
42. Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Zeping Yu, Yonatan Belinkov, Sophia Ananiadou2025 · 16 citations
43. Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhisong Zhang, Yan Wang, Xinting Huang, et al.2025 · 14 citations
44. Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Lei Huang, Xiaocheng Feng, Weitao Ma, et al.2025 · 11 citations
45. Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Hui Liu, Wenya Wang, Hao Sun, et al.2025 · 11 citations
46. 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
47. Paths Not Taken: Understanding and Mending the Multilingual Factual Recall PipelineConference on Empirical Methods in Natural Language Processing (EMNLP)Meng Lu, Ruochen Zhang, Carsten Eickhoff, et al.2025 · 9 citations
48. 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
49. Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model InterpretabilityInternational Conference on Computational Linguistics (COLING)Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.2025 · 8 citations
50. Just read twice: closing the recall gap for recurrent language modelsarXiv:2407.05483Simran Arora; Aman Timalsina; Aaryan Singhal; Benjamin Spector; Sabri Eyuboglu; Xinyi Zhao; Ashish Rao; Atri Rudra;, Christopher Re2024 · 8 citations
51. The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation AnalysisComputational LinguisticsAaron Mueller, Jannik Brinkmann, Millicent Li, et al.2026 · 7 citations
52. ICLEval: Evaluating In-Context Learning Ability of Large Language ModelsInternational Conference on Computational Linguistics (COLING)Wentong Chen, Yankai Lin, ZhenHao Zhou, et al.2025 · 7 citations
53. Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Zhipeng Yang, Junzhuo Li, Siyu Xia, et al.2025 · 7 citations
54. Are Formal and Functional Linguistic Mechanisms Dissociated in Language Models?Computational LinguisticsMichael Hanna, Yonatan Belinkov, Sandro Pezzelle2026 · 5 citations
55. Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path AnchoringAnnual Meeting of the Association for Computational Linguistics (ACL)Dongxu Zhang, Yiding Sun, Cheng Tan, et al.2026 · 3 citations
56. In-Context Representation HijackingAnnual Meeting of the Association for Computational Linguistics (ACL)Itay Yona, Amir Sarid, Michael Karasik, et al.2026 · 2 citations
57. Mechanisms of Prompt-Induced Hallucination in Vision–Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)William Rudman, Michal Golovanevsky, Dana Arad, et al.2026 · 2 citations
58. ToMMeR - Efficient Entity Mention Detection from Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Victor Morand, Nadi Tomeh, Josiane Mothe, et al.2026 · 2 citations
59. Hypothesis-only Biases in Large Language Model-Elicited Natural Language InferencearXiv:2410.08996Grace Proebsting, Adam Poliak2024 · 2 citations
60. Diffusion-CAM: Faithful Visual Explanations for dMLLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Haomin Zuo, Yidi Li, Luoxiao Yang, et al.2026 · 1 citation
61. Multi-component Causal Tracing in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zirui Yan, Dennis Wei, Dmitriy A Katz, et al.2026 · 1 citation
62. ReAttn: Improving Attention-based Re-ranking via Attention Re-weightingFindings of the Association for Computational LinguisticsYuxing Tian, Fengran Mo, Weixu Zhang, et al.2026 · 1 citation
63. SSA: Improving Performance With a Better Scoring FunctionAnnual Meeting of the Association for Computational Linguistics (ACL)Omar Naim, Swarnadeep Bhar, Jerome Bolte, et al.2026 · 1 citation
64. Start Making Sense(s): A Developmental Probe of Attention Specialization Using Lexical AmbiguityTransactions of the Association for Computational Linguistics (TACL)Pamela D. Rivière, Sean Trott2026 · 1 citation
65. Biases in Large Language Model-Elicited Text: A Case Study in Natural Language InferenceInternational Conference on Computational Linguistics (COLING)Grace Proebsting, Adam Poliak2025 · 1 citation
66. Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve TasksInternational Workshop on Construction Grammars and NLPHarish Tayyar Madabushi, Claire Bonial2025 · 1 citation
67. Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language ModelsConference on Computational Natural Language Learning (CoNLL)Nura Aljaafari, Danilo Carvalho, Andre Freitas2026
68. 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
69. Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden StatesWorkshop on Trustworthy NLPSubramanyam Sahoo, Vinija Jain, Aman Chadha, et al.2026
70. Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit FrameworkLanguage Resources and Evaluation Conference (LREC)Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.2026
71. On the Additive Compositionality of Task Vectors in Vision–Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Yuting Shi, Houjing Wei, Naoya Inoue2026
72. Soft Head Selection for Injecting ICL-Derived Task EmbeddingsFindings of the Association for Computational LinguisticsJungwon Park, Jimyeong Kim, Changin Choi, et al.2026
73. Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid ArchitecturesFindings of the Association for Computational LinguisticsShenran Wang, Timothy Tin-Long Tse, Jian Zhu2026
74. XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake DetectionFindings of the Association for Computational LinguisticsKwok-Ho Ng, Tingting Song, Yongdong WU, et al.2026

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