🧑‍🔬 Atticus Geiger — all works

30 works · ← Atticus Geiger

1. Dynabench: Rethinking Benchmarking in NLP ↗Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetDouwe Kiela et al.2021 · 482 citations
2. Causal Abstractions of Neural Networks ↗arXiv:2106.02997 · no summary yetAtticus Geiger et al.2021 · 151 citations
3. Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and NegationAtticus Geiger, Kyle Richardson, Christopher PottsEnglish · 123 citations
4. DynaSent: A Dynamic Benchmark for Sentiment Analysis ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetChristopher Potts et al.2021 · 103 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. Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations ↗arXiv:2303.02536 · no summary yetAtticus Geiger et al.2023 · 81 citations
7. ReFT: Representation Finetuning for Language Models ↗arXiv:2404.03592 · no summary yetZhengxuan Wu et al.2024 · 74 citations
8. Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability ↗arXiv:2301.04709 · no summary yetAtticus Geiger et al.2023 · 65 citations
9. Linear Representations of Sentiment in Large Language Models ↗arXiv:2310.15154 · no summary yetCurt Tigges et al.2023 · 64 citations
10. Posing Fair Generalization Tasks for Natural Language InferenceConference on Empirical Methods in Natural Language Processing (EMNLP)Atticus Geiger, Ignacio Cases, Lauri Karttunen, et al.2019 · 61 citations
11. AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders ↗arXiv:2501.17148 · no summary yetZhengxuan Wu et al.2025 · 59 citations
12. Inducing Causal Structure for Interpretable Neural Networks ↗arXiv (Cornell University) · no summary yetAtticus Geiger et al.2021 · 53 citations
13. RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJing Huang et al.2024 · 35 citations
14. 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
15. CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior ↗arXiv (Cornell University) · no summary yetEldar David Abraham et al.2022 · 29 citations
16. ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetJingyuan S. She et al.2023 · 27 citations
17. Causal Proxy Models for Concept-Based Model Explanations ↗arXiv (Cornell University) · no summary yetZhengxuan Wu et al.2022 · 27 citations
18. pyvene: A Library for Understanding and Improving PyTorch Models via Interventions ↗arXiv (Cornell University) · no summary yetZhengxuan Wu et al.2024 · 26 citations
19. Causal Distillation for Language Models ↗Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetZhengxuan Wu et al.2022 · 25 citations
20. Relational reasoning and generalization using nonsymbolic neural networks. ↗Psychological Review · no summary yetAtticus Geiger et al.2020 · 19 citations
21. Recursive Routing Networks: Learning to Compose Modules for Language Understanding ↗Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · no summary yetIgnacio Cases et al.2019 · 19 citations
22. Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations ↗Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP · no summary yetRóbert Csordás et al.2024 · 16 citations
23. 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
24. \textttpyvene: A Library for Understanding and Improving PyTorch Models via Interventions ↗Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations) · no summary yetZhengxuan Wu et al.2024 · 7 citations
25. A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments ↗arXiv (Cornell University) · no summary yetZhengxuan Wu et al.2024 · 5 citations
26. Updating CLIP to Prefer Descriptions Over Captions ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · no summary yetAmir Zur et al.2024 · 5 citations
27. HyperSteer: Activation Steering at Scale with Hypernetworks ↗arXiv (Cornell University) · no summary yetSun, Jiuding et al.2025 · 4 citations
28. HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks ↗arXiv (Cornell University) · no summary yetJiuding Sun et al.2025 · 4 citations
29. Constructing Interpretable Features from Compositional Neuron GroupsAnnual Meeting of the Association for Computational Linguistics (ACL)Or David Shafran, Atticus Geiger, Mor Geva2026 · 1 citation
30. Mechanistic interpretability and control for language models ↗Stanford Digital Repository · no summary yetZhengxuan Wu et al.2026

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