Geva, Mor

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Transformer Feed-Forward Layers Are Key-Value Memories

Geva, M., Schuster, R., Berant, J., & Levy, O. (2020) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 353

Transformer Feed-Forward Layers Are Key-Value Memories

Geva, M., Schuster, R., Berant, J., & Levy, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 353

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

Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets

Geva, M., Goldberg, Y., & Berant, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 284

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., & Berant, J. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 242

Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space

Geva, M., Caciularu, A., Wang, K. R., & Goldberg, Y. (2022) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 123

LM vs LM: Detecting Factual Errors via Cross Examination

Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023) · arXiv

Topic ModelingMisinformation and Its Impacts

Cited by 85

LM vs LM: Detecting Factual Errors via Cross Examination

Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingMisinformation and Its Impacts

Cited by 60

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Geva, M., Bastings, J., Filippova, K., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 53

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Geva, M., Bastings, J., Filippova, K., & Globerson, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

SCROLLS: Standardized CompaRison Over Long Language Sequences

Shaham, U., Segal, E., Ivgi, M., Efrat, A., Yoran, O., Haviv, A., Gupta, A., Xiong, W., Geva, M., Berant, J., et al. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 40

Estimating Knowledge in Large Language Models Without Generating a Single Token

Gottesman, D., & Geva, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 32

Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

Yona, G., Aharoni, R., & Geva, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 18

A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

Jacovi, A., Bitton, Y., Bohnet, B., Herzig, J., Honovich, O., Tseng, M., Collins, M., Aharoni, R., & Geva, M. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 14

Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries

Biran, E., Gottesman, D., Yang, S., Geva, M., & Globerson, A. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0

Do Large Language Models Latently Perform Multi-Hop Reasoning?

Yang, S., Gribovskaya, E., Kassner, N., Geva, M., & Riedel, S. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0