Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1181 | 2022 |
Transformer feed-forward layers are key-value memories M Geva, R Schuster, J Berant, O Levy arXiv preprint arXiv:2012.14913, 2020 | 623* | 2020 |
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies M Geva, D Khashabi, E Segal, T Khot, D Roth, J Berant Transactions of the Association for Computational Linguistics 9, 346-361, 2021 | 518 | 2021 |
Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets M Geva, Y Goldberg, J Berant EMNLP 2019, 2019 | 369 | 2019 |
Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space M Geva, A Caciularu, KR Wang, Y Goldberg arXiv preprint arXiv:2203.14680, 2022 | 248 | 2022 |
Injecting Numerical Reasoning Skills into Language Models M Geva, A Gupta, J Berant ACL 2020, 2020 | 229 | 2020 |
Break It Down: A Question Understanding Benchmark T Wolfson, M Geva, A Gupta, M Gardner, Y Goldberg, D Deutch, J Berant TACL, 2020 | 184 | 2020 |
Dissecting recall of factual associations in auto-regressive language models M Geva, J Bastings, K Filippova, A Globerson arXiv preprint arXiv:2304.14767, 2023 | 164 | 2023 |
Scrolls: Standardized comparison over long language sequences U Shaham, E Segal, M Ivgi, A Efrat, O Yoran, A Haviv, A Gupta, W Xiong, ... arXiv preprint arXiv:2201.03533, 2022 | 124 | 2022 |
Analyzing transformers in embedding space G Dar, M Geva, A Gupta, J Berant arXiv preprint arXiv:2209.02535, 2022 | 104 | 2022 |
Evaluating the ripple effects of knowledge editing in language models R Cohen, E Biran, O Yoran, A Globerson, M Geva Transactions of the Association for Computational Linguistics 12, 283-298, 2024 | 101 | 2024 |
Lm vs lm: Detecting factual errors via cross examination R Cohen, M Hamri, M Geva, A Globerson arXiv preprint arXiv:2305.13281, 2023 | 90 | 2023 |
In-context learning creates task vectors R Hendel, M Geva, A Globerson arXiv preprint arXiv:2310.15916, 2023 | 83 | 2023 |
Crawling the internal knowledge-base of language models R Cohen, M Geva, J Berant, A Globerson arXiv preprint arXiv:2301.12810, 2023 | 69 | 2023 |
Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions M Parmar, S Mishra, M Geva, C Baral arXiv preprint arXiv:2205.00415, 2022 | 62 | 2022 |
DiscoFuse: A Large-Scale Dataset for Discourse-based Sentence Fusion M Geva, E Malmi, I Szpektor, J Berant NAACL-HLT 2019 1, 3443-3455, 2019 | 55 | 2019 |
Emergence of communication in an interactive world with consistent speakers B Bogin, M Geva, J Berant arXiv preprint arXiv:1809.00549, 2018 | 46* | 2018 |
Lm-debugger: An interactive tool for inspection and intervention in transformer-based language models M Geva, A Caciularu, G Dar, P Roit, S Sadde, M Shlain, B Tamir, ... arXiv preprint arXiv:2204.12130, 2022 | 41 | 2022 |
Jump to Conclusions: Short-Cutting Transformers With Linear Transformations A Yom Din, T Karidi, L Choshen, M Geva arXiv e-prints, arXiv: 2303.09435, 2023 | 38* | 2023 |
Do Large Language Models Latently Perform Multi-Hop Reasoning? S Yang, E Gribovskaya, N Kassner, M Geva, S Riedel arXiv preprint arXiv:2402.16837, 2024 | 35 | 2024 |