Geiger, Atticus

Open Problems in Mechanistic Interpretability

Sharkey, L., Chughtai, B., Batson, J., Lindsey, J., Wu, J., Bushnaq, L., Goldowsky-Dill, N., Heimersheim, S., Ortega, A., Bloom, J., et al. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Neural Networks and Applications

Cited by 291

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 254

DynaSent: A Dynamic Benchmark for Sentiment Analysis

Potts, C., Wu, Z., Geiger, A., & Kiela, D. (2021) · 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)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 56

Causal abstractions of neural networks

Geiger, A., Lu, H., Icard, T., & Potts, C. (2021) · arXiv

Explainable Artificial Intelligence (XAI)Bayesian Modeling and Causal Inference

Cited by 45

Interpretability at Scale: Identifying Causal Mechanisms in Alpaca

Wu, Z., Geiger, A., Icard, T., Potts, C., & Goodman, N. D. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Philosophy of Biology and Scientific Explanation

Cited by 14

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

Geiger, A., Ibeling, D., Zur, A., Chaudhary, M., Chauhan, S., Huang, J., Arora, A., Wu, Z., Goodman, N., Potts, C., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 11

Rigorously assessing natural language explanations of neurons

Huang, J., Geiger, A., D’Oosterlinck, K., Wu, Z., & Potts, C. (2023) · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 3

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Wu, Z., Arora, A., Geiger, A., Wang, Z., Huang, J., Jurafsky, D., Manning, C. D., & Potts, C. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 0