---
title: "Who cites Arthur Conmy — 538 papers of our library"
description: "538 papers of the Elamur Research library cite the works of Arthur Conmy, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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# Who cites Arthur Conmy

697 citations of their works from 538 papers of our library (of the 149,439 whose bibliographies we read) · 132 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Arthur Conmy](https://md.research.elamur.ai/free/en/authors/arthur-conmy/)

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1. [**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/)
2. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
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. [**Eliciting Latent Predictions from Transformers with the Tuned Lens arXiv:2303.08112**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
5. [**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/)
6. **[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*
7. **[PMET: Precise Model Editing in a Transformer](https://md.research.elamur.ai/free/en/papers/li-2023-pmet-precise-model-editing-transformer/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Xiaopeng Li; Shasha Li; Shezheng Song; Jing Yang; Jun Ma;, Jie Yu*2023 · 135 citations*
8. [**The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning arXiv:2403.03218**Nathaniel Li, Alexander Pan, Anjali Gopal, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/)
9. [**Function Vectors in Large Language Models arXiv:2310.15213**Eric Todd, Millicent L. Li, Arnab Sen Sharma, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/todd-2023-function-vectors-large-language-models/)
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. [**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/)
12. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
13. [**Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 arXiv:2408.05147**Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, et al.*2024 · 115 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2024-gemma-scope-open-sparse-autoencoders/)
14. [**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/)
15. [**Reasoning Models Don't Always Say What They Think arXiv:2505.05410**Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.*2025 · 106 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-reasoning-models-don-t-always/)
16. [**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/)
17. [**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/)
18. [**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/)
19. [**Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models arXiv:2401.06102**Asma Ghandeharioun, Avi Caciularu, Adam Pearce, et al.*2024 · 84 citations*](https://md.research.elamur.ai/free/en/papers/ghandeharioun-2024-patchscopes-unifying-framework-inspecting-hidden/)
20. [**Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations arXiv:2303.02536**Atticus Geiger, Zhengxuan Wu, Christopher Potts, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2023-finding-alignments-between-interpretable-causal/)
21. **[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*
22. [**Localizing Model Behavior with Path Patching arXiv:2304.05969**Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, et al.*2023 · 76 citations*](https://md.research.elamur.ai/free/en/papers/goldowsky-dill-2023-localizing-model-behavior-path-patching/)
23. **[ReFT: Representation Finetuning for Language Models](https://md.research.elamur.ai/free/en/papers/wu-2024-reft-representation-finetuning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Aryaman Arora, Zheng Wang, et al.*2024 · 74 citations*
24. [**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/)
25. [**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/)
26. [**Phi-4-reasoning Technical Report arXiv:2504.21318**Marah Abdin, Sahaj Agarwal, Ahmed Awadallah, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2025-phi-4-reasoning-technical-report/)
27. [**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/)
28. [**Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders arXiv:2410.20526**Zhengfu He, Wentao Shu, Xuyang Ge, et al.*2024 · 63 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-llama-scope-extracting-millions-features/)
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. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
31. **[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*
32. [**Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics arXiv:2410.21272**Yaniv Nikankin, Anja Reusch, Aaron Mueller, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/nikankin-2024-arithmetic-algorithms-language-models-solve/)
33. **[Information Flow Routes: Automatically Interpreting Language Models at Scale](https://md.research.elamur.ai/free/en/papers/ferrando-2024-information-flow-routes-automatically-interpreting/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Javier Ferrando, Elena Voita*2024 · 43 citations*
34. **[CausalGym: Benchmarking causal interpretability methods on linguistic tasks](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aryaman Arora, Dan Jurafsky, Christopher Potts*2024 · 38 citations*
35. **[Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://md.research.elamur.ai/free/en/papers/suzgun-2026-dynamic-cheatsheet-test-time-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, et al.*2026 · 37 citations*
36. **[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*
37. **[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*
38. [**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/)
39. [**Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in? arXiv:2408.10811**Chengzhi Zhong, Fei Cheng, Qianying Liu, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-english-centric-llms-language-multilingual/)
40. **[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*
41. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
42. [**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/)
43. **[Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/deng-2025-unveiling-language-specific-features-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boyi Deng, Yu Wan, Baosong Yang, et al.*2025 · 18 citations*
44. [**Locating and Editing Factual Associations in Mamba arXiv:2404.03646**Arnab Sen Sharma, David Atkinson, David Bau*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2024-locating-editing-factual-associations-mamba/)
45. **[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*
46. [**A Survey on Latent Reasoning arXiv:2507.06203**Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-survey-latent-reasoning/)
47. **[Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps](https://md.research.elamur.ai/free/en/papers/tutek-2025-measuring-chain-thought-faithfulness-unlearning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasovic, et al.*2025 · 15 citations*
48. **[Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms](https://md.research.elamur.ai/free/en/papers/wang-2025-beyond-prompt-engineering-robust-behavior/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ziwen Xu, Shengyu Mao, et al.*2025 · 14 citations*
49. [**Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions**Sasha Boguraev, Christopher Potts, Kyle Mahowald*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/boguraev-2025-causal-interventions-reveal-shared-structure/)
50. **[Enhancing Automated Interpretability with Output-Centric Feature Descriptions](https://md.research.elamur.ai/free/en/papers/arieh-2025-enhancing-automated-interpretability-output-centric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yoav Gur-Arieh, Roy Mayan, Chen Agassy, et al.*2025 · 13 citations*
51. **[Personalized Text Generation with Contrastive Activation Steering](https://md.research.elamur.ai/free/en/papers/zhang-2025-personalized-text-generation-contrastive-activation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinghao Zhang, Yuting Liu, Wenjie Wang, et al.*2025 · 13 citations*
52. **[SAEs Are Good for Steering – If You Select the Right Features](https://md.research.elamur.ai/free/en/papers/arad-2025-saes-are-good-steering-if/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dana Arad, Aaron Mueller, Yonatan Belinkov*2025 · 12 citations*
53. **[Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/li-2025-feature-extraction-steering-enhanced-chain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zihao Li, Xu Wang, Yuzhe Yang, et al.*2025 · 11 citations*
54. **[Route Sparse Autoencoder to Interpret Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2025-route-sparse-autoencoder-interpret-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Shi, Sihang Li, Tao Liang, et al.*2025 · 11 citations*
55. **[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*
56. **[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*
57. **[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*
58. **[Model Unlearning via Sparse Autoencoder Subspace Guided Projections](https://md.research.elamur.ai/free/en/papers/wang-2025-model-unlearning-sparse-autoencoder-subspace/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xu Wang, Zihao Li, Benyou Wang, et al.*2025 · 8 citations*
59. **[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*
60. **[Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis](https://md.research.elamur.ai/free/en/papers/boughorbel-2024-improving-language-models-trained-translated/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sabri Boughorbel, Md Rizwan Parvez, Majd Hawasly*2024 · 8 citations*
61. **[A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages](https://md.research.elamur.ai/free/en/papers/zhao-2026-comprehensive-evaluation-multilingual-chain-thought/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Raoyuan Zhao, Yihong Liu, Hinrich Schuetze, et al.*2026 · 7 citations*
62. **[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*
63. **[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*
64. **[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*
65. **[The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It](https://md.research.elamur.ai/free/en/papers/bertolazzi-2025-validation-gap-mechanistic-analysis-how/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Leonardo Bertolazzi, Philipp Mondorf, Barbara Plank, et al.*2025 · 6 citations*
66. **[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*
67. **[KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality](https://md.research.elamur.ai/free/en/papers/ren-2026-knowrl-exploring-knowledgeable-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baochang Ren, Shuofei Qiao, Ningyu Zhang, et al.*2026 · 5 citations*
68. **[Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?](https://md.research.elamur.ai/free/en/papers/lim-2025-analysing-chain-thought-dynamics-active/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao, et al.*2025 · 5 citations*
69. **[Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering](https://md.research.elamur.ai/free/en/papers/zhao-2026-denoising-concept-vectors-sparse-autoencoders/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiyan Zhao, Xuansheng Wu, Fan Yang, et al.*2026 · 3 citations*
70. [**Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models arXiv:2605.11887**Boyi Deng, Xu Wang, Yaoning Wang, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/deng-2026-qwen-scope-turning-sparse/)
71. **[Steering Large Language Models for Machine Translation Personalization](https://md.research.elamur.ai/free/en/papers/scalena-2026-steering-large-language-models-machine/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Daniel Scalena, Gabriele Sarti, Arianna Bisazza, et al.*2026 · 3 citations*
72. **[Universal Jailbreak Suffixes Are Strong Attention Hijackers](https://md.research.elamur.ai/free/en/papers/tov-2026-universal-jailbreak-suffixes-are-strong/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Matan Ben-Tov, Mor Geva, Mahmood Sharif*2026 · 3 citations*
73. **[AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor](https://md.research.elamur.ai/free/en/papers/yang-2026-automonitor-bench-evaluating-reliability-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shu Yang, Jingyu Hu, Tong Li, et al.*2026 · 2 citations*
74. **[C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/mittal-2026-c2-faith-benchmarking-llm-judges/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Avni Mittal, Rauno Arike*2026 · 2 citations*
75. **[Do LLMs Encode Functional Importance of Reasoning Tokens ?](https://md.research.elamur.ai/free/en/papers/singh-2026-do-llms-encode-functional-importance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Janvijay Singh, Dilek Hakkani-Tür*2026 · 2 citations*
76. **[LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals](https://md.research.elamur.ai/free/en/papers/sun-2026-llm-reasoning-as-trajectories-step/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lihao Sun, Hang Dong, Bo Qiao, et al.*2026 · 2 citations*
77. **[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*
78. **[SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection](https://md.research.elamur.ai/free/en/papers/zhang-2026-sae-fire-enhancing-earnings-surprise/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huopu Zhang, Yanguang Liu, Miao Zhang, et al.*2026 · 2 citations*
79. **[Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework](https://md.research.elamur.ai/free/en/papers/weng-2026-safe-sail-fine-grained-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaqi Weng, Han Zheng, Hanyu Zhang, et al.*2026 · 2 citations*
80. **[Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://md.research.elamur.ai/free/en/papers/havens-2025-investigating-capabilities-limitations-machine-learning/) [CHI Conference on Human Factors in Computing Systems](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/)**Lucy Havens, Benjamin Bach, Melissa Terras, et al.*2025 · 2 citations*
81. **[A Comprehensive Evaluation of Chain-of-Thought Faithfulness in Persian Classification Tasks](https://md.research.elamur.ai/free/en/papers/yazdani-2026-comprehensive-evaluation-chain-thought-faithfulness/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Shakib Yazdani, Cristina España-Bonet, Eleftherios Avramidis, et al.*2026 · 1 citation*
82. **[Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics](https://md.research.elamur.ai/free/en/papers/shihab-2026-beyond-variance-knowledge-aware-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma*2026 · 1 citation*
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87. **[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*
88. **[SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/lv-2026-spd-faith-bench-diagnosing-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weijiang Lv, Yaoxuan Feng, Xiaobo Xia, et al.*2026 · 1 citation*
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90. **[Tug-of-war between idioms' figurative and literal interpretations in LLMs](https://md.research.elamur.ai/free/en/papers/oh-2026-tug-war-between-idioms-figurative/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Soyoung Oh, Xinting Huang, Mathis Pink, et al.*2026 · 1 citation*
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93. **[Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains](https://md.research.elamur.ai/free/en/papers/park-2026-where-reasoning-breaks-logic-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seunghyun Park, Yuanyuan Lei*2026 · 1 citation*
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96. **[Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/yan-2026-beyond-cross-modal-alignment-measuring/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanqi Yan, Xiangxiang Cui, Lu Yin, et al.*2026*
97. **[Beyond the Token: Correcting the Tokenization Bias in XAI via Morphologically-Aligned Projection](https://md.research.elamur.ai/free/en/papers/yagiz-2026-beyond-token-correcting-tokenization-bias/) [Workshop Natural Language Processing for Turkic Languages](https://md.research.elamur.ai/free/en/venues/sigturk/)**Muhammet Anil Yagiz, Fahrettin Horasan*2026*
98. **[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*
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100. **[DKME: Rethinking Coupled Knowledge Memory for Lifelong Model Editing of Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-dkme-rethinking-coupled-knowledge-memory/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guanyu Zheng, Wang Zhenyu, He Tingting, et al.*2026*

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