🔗 Who cites «Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations»

73 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · ← back to «Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations»

1. Refusal in Language Models Is Mediated by a Single DirectionarXiv:2406.11717Andy Arditi, Oscar Obeso, Aaquib Syed, et al.2024 · 196 citations
2. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
3. Towards Best Practices of Activation Patching in Language Models: Metrics and MethodsarXiv:2309.16042Fred Zhang, Neel Nanda2023 · 97 citations
4. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
5. 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
6. Localizing Model Behavior with Path PatchingarXiv:2304.05969Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, et al.2023 · 76 citations
7. ReFT: Representation Finetuning for Language ModelsConference on Neural Information Processing Systems (NeurIPS)Zhengxuan Wu, Aryaman Arora, Zheng Wang, et al.2024 · 74 citations
8. A Primer on the Inner Workings of Transformer-based Language ModelsarXiv:2405.00208Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.2024 · 72 citations
9. Mission: Impossible Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Julie Kallini, Isabel Papadimitriou, Richard Futrell, et al.2024 · 70 citations
10. Linear Representations of Sentiment in Large Language ModelsarXiv:2310.15154Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, et al.2023 · 64 citations
11. Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in ChinchillaarXiv:2307.09458Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.2023 · 58 citations
12. CausalGym: Benchmarking causal interpretability methods on linguistic tasksAnnual Meeting of the Association for Computational Linguistics (ACL)Aryaman Arora, Dan Jurafsky, Christopher Potts2024 · 38 citations
13. 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
14. LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-SteeringAnnual Meeting of the Association for Computational Linguistics (ACL)Jinhe Bi, Yujun Wang, Haokun Chen, et al.2025 · 24 citations
15. Causal Interventions Reveal Shared Structure Across English Filler–Gap ConstructionsSasha Boguraev, Christopher Potts, Kyle Mahowald2025 · 13 citations
16. Enhancing Automated Interpretability with Output-Centric Feature DescriptionsAnnual Meeting of the Association for Computational Linguistics (ACL)Yoav Gur-Arieh, Roy Mayan, Chen Agassy, et al.2025 · 13 citations
17. Localized Cultural Knowledge is Conserved and Controllable in Large Language ModelsFindings of the Association for Computational LinguisticsVeniamin Veselovsky, Berke Argın, Benedikt Stroebl, et al.2026 · 7 citations
18. 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
19. Constructing Interpretable Features from Compositional Neuron GroupsAnnual Meeting of the Association for Computational Linguistics (ACL)Or David Shafran, Atticus Geiger, Mor Geva2026 · 1 citation
20. A framework for analyzing concept representations in neural modelsConference on Computational Natural Language Learning (CoNLL)Burin Naowarat, Hao Tang, Sharon Goldwater2026
21. Bears, all bears, and some bears. Language Constraints on Language Models' Inductive InferencesFindings of the Association for Computational LinguisticsSriram Padmanabhan, Siyuan Song, Kanishka Misra2026
22. FedReFT: Federated Representation Fine-Tuning with All-But-Me AggregationFindings of the Association for Computational LinguisticsFatema Siddika, Md Anwar Hossen, Juan Pablo Munoz, et al.2026
23. From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ling Shi, Xinwei Wu, Xiaohu Zhao, et al.2026
24. When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question AnsweringFindings of the Association for Computational LinguisticsHugh Mee Wong, Rick Nouwen, Albert Gatt2026

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

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