---
title: "Who cites In-context Learning and — 391 citing papers"
description: "391 papers of the Elamur Research library cite «In-context Learning and Induction Heads», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [In-context Learning and Induction Heads](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/) / cited by

# 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»](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/)

1. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
2. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv:2303.12712**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,439 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
3. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
4. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
5. **[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*
6. [**STaR: Bootstrapping Reasoning With Reasoning**Eric Zelikman, Yuhuai Wu, Jesse Mu, et al.*2022 · 777 citations*](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)
7. [**RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/)
8. [**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/)
9. [**The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery arXiv:2408.06292**Chris Lu, Cong Lu, Robert Tjarko Lange, et al.*2024 · 372 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-ai-scientist-fully-automated/)
10. **[SnapKV: LLM Knows What You are Looking for Before Generation](https://md.research.elamur.ai/free/en/papers/li-2024-snapkv-llm-knows-looking-before/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuhong Li, Yingbing Huang, Bowen Yang, et al.*2024 · 355 citations*
11. [**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/)
12. **[Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 257 citations*
13. [**Transformers learn in-context by gradient descent arXiv:2212.07677**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 247 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
14. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
15. [**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/)
16. [**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/)
17. [**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/)
18. [**Jamba: A Hybrid Transformer-Mamba Language Model arXiv:2403.19887**Opher Lieber, Barak Lenz, Hofit Bata, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/lieber-2024-jamba-hybrid-transformer-mamba-language/)
19. **[In-Context Learning Creates Task Vectors](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Roee Hendel, Mor Geva, Amir Globerson*2023 · 159 citations*
20. **[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*
21. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations*
22. [**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/)
23. [**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/)
24. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jane Pan, Tianyu Gao, Howard Chen, et al.*2023 · 102 citations*
25. [**Retrieval Head Mechanistically Explains Long-Context Factuality arXiv:2404.15574**Wenhao Wu, Yizhong Wang, Guangxuan Xiao, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-head-mechanistically-explains-long/)
26. [**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/)
27. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.*2023 · 81 citations*
28. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
29. [**Simple linear attention language models balance the recall-throughput tradeoff arXiv:2402.18668**Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/)
30. [**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/)
31. [**Zoology: Measuring and Improving Recall in Efficient Language Models arXiv:2312.04927**Simran Arora; Sabri Eyuboglu; Aman Timalsina; Isys Johnson; Michael Poli; James Zou; Atri Rudra;, Christopher Re*2023 · 55 citations*](https://md.research.elamur.ai/free/en/papers/arora-2023-zoology-measuring-improving-recall-efficient/)
32. **[The Learnability of In-Context Learning](https://md.research.elamur.ai/free/en/papers/wies-2023-learnability-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Noam Wies, Yoav Levine, Amnon Shashua*2023 · 53 citations*
33. [**The MiniPile Challenge for Data-Efficient Language Models arXiv:2304.08442**Jean Kaddour*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-minipile-challenge-data-efficient-language/)
34. [**Through the Lens of Core Competency: Survey on Evaluation of Large Language Models arXiv:2308.07902**Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-through-lens-core-competency-survey/)
35. **[Rigorously Assessing Natural Language Explanations of Neurons](https://md.research.elamur.ai/free/en/papers/huang-2023-rigorously-assessing-natural-language-explanations/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Jing Huang, Atticus Geiger, Karel D'Oosterlinck, et al.*2023 · 30 citations*
36. [**The Quantization Model of Neural Scaling arXiv:2303.13506**Eric J. Michaud, Ziming Liu, Uzay Girit, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-neural-scaling/)
37. [**RWKV-7 "Goose" with Expressive Dynamic State Evolution arXiv:2503.14456**Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/peng-2025-rwkv-7-goose-expressive-dynamic/)
38. **[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*
39. **[TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration](https://md.research.elamur.ai/free/en/papers/li-2025-taco-enhancing-multimodal-context-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yanshu Li, Jianjiang Yang, Tian Yun, et al.*2025 · 22 citations*
40. [**Personality Alignment of Large Language Models arXiv:2408.11779**Minjun Zhu, Yixuan Weng, Linyi Yang, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-personality-alignment-large-language-models/)
41. [**Understanding Emergent In-Context Learning from a Kernel Regression Perspective arXiv:2305.12766**Chi Han, Ziqi Wang, Han Zhao, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-understanding-emergent-context-learning-kernel/)
42. **[Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/yu-2025-back-attention-understanding-enhancing-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zeping Yu, Yonatan Belinkov, Sophia Ananiadou*2025 · 16 citations*
43. **[Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-attention-entropy-is-key-factor/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhisong Zhang, Yan Wang, Xinting Huang, et al.*2025 · 14 citations*
44. **[Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization](https://md.research.elamur.ai/free/en/papers/huang-2025-improving-contextual-faithfulness-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Huang, Xiaocheng Feng, Weitao Ma, et al.*2025 · 11 citations*
45. **[Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2025-unraveling-mechanics-learning-based-demonstration/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 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*
47. **[Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline](https://md.research.elamur.ai/free/en/papers/lu-2025-paths-not-taken-understanding-mending/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Meng Lu, Ruochen Zhang, Carsten Eickhoff, et al.*2025 · 9 citations*
48. **[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*
49. **[Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability](https://md.research.elamur.ai/free/en/papers/duan-2025-unveiling-language-competence-neurons-psycholinguistic/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.*2025 · 8 citations*
50. [**Just read twice: closing the recall gap for recurrent language models arXiv:2407.05483**Simran Arora; Aman Timalsina; Aaryan Singhal; Benjamin Spector; Sabri Eyuboglu; Xinyi Zhao; Ashish Rao; Atri Rudra;, Christopher Re*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/arora-2024-just-read-twice-closing-recall/)
51. **[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*
52. **[ICLEval: Evaluating In-Context Learning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-icleval-evaluating-context-learning-ability/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/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 LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-internal-chain-thought-empirical-evidence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhipeng Yang, Junzhuo Li, Siyu Xia, et al.*2025 · 7 citations*
54. **[Are Formal and Functional Linguistic Mechanisms Dissociated in Language Models?](https://md.research.elamur.ai/free/en/papers/hanna-2026-are-formal-functional-linguistic-mechanisms/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Michael Hanna, Yonatan Belinkov, Sandro Pezzelle*2026 · 5 citations*
55. **[Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring](https://md.research.elamur.ai/free/en/papers/zhang-2026-chain-thought-compression-should-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dongxu Zhang, Yiding Sun, Cheng Tan, et al.*2026 · 3 citations*
56. **[In-Context Representation Hijacking](https://md.research.elamur.ai/free/en/papers/yona-2026-context-representation-hijacking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Itay Yona, Amir Sarid, Michael Karasik, et al.*2026 · 2 citations*
57. **[Mechanisms of Prompt-Induced Hallucination in Vision–Language Models](https://md.research.elamur.ai/free/en/papers/rudman-2026-mechanisms-prompt-induced-hallucination-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**William Rudman, Michal Golovanevsky, Dana Arad, et al.*2026 · 2 citations*
58. **[ToMMeR - Efficient Entity Mention Detection from Large Language Models](https://md.research.elamur.ai/free/en/papers/morand-2026-tommer-efficient-entity-mention-detection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Victor Morand, Nadi Tomeh, Josiane Mothe, et al.*2026 · 2 citations*
59. [**Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference arXiv:2410.08996**Grace Proebsting, Adam Poliak*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/proebsting-2024-hypothesis-only-biases-large-language/)
60. **[Diffusion-CAM: Faithful Visual Explanations for dMLLMs](https://md.research.elamur.ai/free/en/papers/zuo-2026-diffusion-cam-faithful-visual-explanations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haomin Zuo, Yidi Li, Luoxiao Yang, et al.*2026 · 1 citation*
61. **[Multi-component Causal Tracing in Large Language Models](https://md.research.elamur.ai/free/en/papers/yan-2026-multi-component-causal-tracing-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zirui Yan, Dennis Wei, Dmitriy A Katz, et al.*2026 · 1 citation*
62. **[ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting](https://md.research.elamur.ai/free/en/papers/tian-2026-reattn-improving-attention-based-re/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxing Tian, Fengran Mo, Weixu Zhang, et al.*2026 · 1 citation*
63. **[SSA: Improving Performance With a Better Scoring Function](https://md.research.elamur.ai/free/en/papers/naim-2026-ssa-improving-performance-better-scoring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Ambiguity](https://md.research.elamur.ai/free/en/papers/riviere-2026-start-making-sense-s-developmental/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Pamela D. Rivière, Sean Trott*2026 · 1 citation*
65. **[Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/proebsting-2025-biases-large-language-model-elicited/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Grace Proebsting, Adam Poliak*2025 · 1 citation*
66. **[Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/) [International Workshop on Construction Grammars and NLP](https://md.research.elamur.ai/free/en/venues/cxgsnlp/)**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*
67. **[Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models](https://md.research.elamur.ai/free/en/papers/aljaafari-2026-bridging-linguistic-structure-mechanistic-interpretability/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Nura Aljaafari, Danilo Carvalho, Andre Freitas*2026*
68. **[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*
69. **[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*
70. **[Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit Framework](https://md.research.elamur.ai/free/en/papers/proietti-2026-mechanistic-interpretability-meets-cognitive-linguistics/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.*2026*
71. **[On the Additive Compositionality of Task Vectors in Vision–Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-additive-compositionality-task-vectors-vision/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuting Shi, Houjing Wei, Naoya Inoue*2026*
72. **[Soft Head Selection for Injecting ICL-Derived Task Embeddings](https://md.research.elamur.ai/free/en/papers/park-2026-soft-head-selection-injecting-icl/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jungwon Park, Jimyeong Kim, Changin Choi, et al.*2026*
73. **[Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures](https://md.research.elamur.ai/free/en/papers/wang-2026-understanding-context-learning-beyond-transformers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shenran Wang, Timothy Tin-Long Tse, Jian Zhu*2026*
74. **[XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection](https://md.research.elamur.ai/free/en/papers/ng-2026-xlsr-mambo-scaling-hybrid-mamba/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kwok-Ho Ng, Tingting Song, Yongdong WU, et al.*2026*

317 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/) — In-context Learning and Induction Heads