| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1482 | 2023 |
| 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 | 999 | 2022 |
| Synthetic and natural noise both break neural machine translation Y Belinkov, Y Bisk arXiv preprint arXiv:1711.02173, 2017 | 825 | 2017 |
| Linguistic knowledge and transferability of contextual representations NF Liu, M Gardner, Y Belinkov, ME Peters, NA Smith arXiv preprint arXiv:1903.08855, 2019 | 805 | 2019 |
| Locating and editing factual associations in GPT K Meng, D Bau, A Andonian, Y Belinkov Advances in Neural Information Processing Systems 35, 17359-17372, 2022 | 780 | 2022 |
| Fine-grained analysis of sentence embeddings using auxiliary prediction tasks Y Adi, E Kermany, Y Belinkov, O Lavi, Y Goldberg arXiv preprint arXiv:1608.04207, 2016 | 623 | 2016 |
| Analysis methods in neural language processing: A survey Y Belinkov, J Glass Transactions of the Association for Computational Linguistics 7, 49-72, 2019 | 584 | 2019 |
| What do neural machine translation models learn about morphology? Y Belinkov, N Durrani, F Dalvi, H Sajjad, J Glass arXiv preprint arXiv:1704.03471, 2017 | 447 | 2017 |
| Investigating gender bias in language models using causal mediation analysis J Vig, S Gehrmann, Y Belinkov, S Qian, D Nevo, Y Singer, S Shieber Advances in neural information processing systems 33, 12388-12401, 2020 | 362 | 2020 |
| Analyzing the structure of attention in a transformer language model J Vig, Y Belinkov arXiv preprint arXiv:1906.04284, 2019 | 359 | 2019 |
| Probing classifiers: Promises, shortcomings, and advances Y Belinkov Computational Linguistics 48 (1), 207-219, 2022 | 347 | 2022 |
| Mass-editing memory in a transformer K Meng, AS Sharma, A Andonian, Y Belinkov, D Bau arXiv preprint arXiv:2210.07229, 2022 | 320 | 2022 |
| Identifying and Controlling Important Neurons in Neural Machine Translation A Bau, Y Belinkov, H Sajjad, N Durrani, F Dalvi, J Glass International Conference on Learning Representations, 2019 | 197 | 2019 |
| What is one grain of sand in the desert? analyzing individual neurons in deep nlp models F Dalvi, N Durrani, H Sajjad, Y Belinkov, A Bau, J Glass Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 6309-6317, 2019 | 190 | 2019 |
| Evaluating layers of representation in neural machine translation on part-of-speech and semantic tagging tasks Y Belinkov, L Màrquez, H Sajjad, N Durrani, F Dalvi, J Glass arXiv preprint arXiv:1801.07772, 2018 | 178* | 2018 |
| A constructive prediction of the generalization error across scales JS Rosenfeld, A Rosenfeld, Y Belinkov, N Shavit arXiv preprint arXiv:1909.12673, 2019 | 173 | 2019 |
| End-to-end bias mitigation by modelling biases in corpora RK Mahabadi, Y Belinkov, J Henderson arXiv preprint arXiv:1909.06321, 2019 | 165 | 2019 |
| Probing the probing paradigm: Does probing accuracy entail task relevance? A Ravichander, Y Belinkov, E Hovy arXiv preprint arXiv:2005.00719, 2020 | 113 | 2020 |
| Analyzing hidden representations in end-to-end automatic speech recognition systems Y Belinkov, J Glass Advances in Neural Information Processing Systems 30, 2017 | 98 | 2017 |
| Analyzing individual neurons in pre-trained language models N Durrani, H Sajjad, F Dalvi, Y Belinkov arXiv preprint arXiv:2010.02695, 2020 | 97 | 2020 |
James GlassMIT Computer Science and Artificial Intelligence Laboratory確認したメール アドレス: mit.edu
