| Ties-merging: Resolving interference when merging models P Yadav, D Tam, L Choshen, CA Raffel, M Bansal Advances in Neural Information Processing Systems 36, 2024 | 260* | 2024 |
| Active learning for BERT: an empirical study LE Dor, A Halfon, A Gera, E Shnarch, L Dankin, L Choshen, M Danilevsky, ... Proceedings of the 2020 conference on empirical methods in natural language …, 2020 | 254 | 2020 |
| An autonomous debating system N Slonim, Y Bilu, C Alzate, R Bar-Haim, B Bogin, F Bonin, L Choshen, ... Nature 591 (7850), 379-384, 2021 | 249 | 2021 |
| : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering O Honovich, L Choshen, R Aharoni, E Neeman, I Szpektor, O Abend arXiv preprint arXiv:2104.08202, 2021 | 189 | 2021 |
| Findings of the BabyLM Challenge: Sample-efficient pretraining on developmentally plausible corpora A Warstadt, A Mueller, L Choshen, E Wilcox, C Zhuang, J Ciro, ... Proceedings of the BabyLM Challenge at the 27th Conference on Computational …, 2023 | 139* | 2023 |
| On the weaknesses of reinforcement learning for neural machine translation L Choshen, L Fox, Z Aizenbud, O Abend arXiv preprint arXiv:1907.01752, 2019 | 94 | 2019 |
| Are you convinced? choosing the more convincing evidence with a Siamese network M Gleize, E Shnarch, L Choshen, L Dankin, G Moshkowich, R Aharonov, ... arXiv preprint arXiv:1907.08971, 2019 | 90 | 2019 |
| Will it blend? blending weak and strong labeled data in a neural network for argumentation mining E Shnarch, C Alzate, L Dankin, M Gleize, Y Hou, L Choshen, R Aharonov, ... Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 84 | 2018 |
| Corpus wide argument mining—a working solution L Ein-Dor, E Shnarch, L Dankin, A Halfon, B Sznajder, A Gera, C Alzate, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7683-7691, 2020 | 81 | 2020 |
| Dora the explorer: Directed outreaching reinforcement action-selection L Choshen, L Fox, Y Loewenstein arXiv preprint arXiv:1804.04012, 2018 | 76 | 2018 |
| Fusing finetuned models for better pretraining L Choshen, E Venezian, N Slonim, Y Katz arXiv preprint arXiv:2204.03044, 2022 | 66 | 2022 |
| Let’s agree to agree: Neural networks share classification order on real datasets G Hacohen, L Choshen, D Weinshall International Conference on Machine Learning, 3950-3960, 2020 | 60 | 2020 |
| Cold fusion: Collaborative descent for distributed multitask finetuning S Don-Yehiya, E Venezian, C Raffel, N Slonim, Y Katz, L Choshen arXiv preprint arXiv:2212.01378, 2022 | 56 | 2022 |
| Disentqa: Disentangling parametric and contextual knowledge with counterfactual question answering E Neeman, R Aharoni, O Honovich, L Choshen, I Szpektor, O Abend arXiv preprint arXiv:2211.05655, 2022 | 55 | 2022 |
| Knowledge is a region in weight space for fine-tuned language models A Gueta, E Venezian, C Raffel, N Slonim, Y Katz, L Choshen arXiv preprint arXiv:2302.04863, 2023 | 47 | 2023 |
| 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 | 45* | 2023 |
| Learning to combine grammatical error corrections Y Kantor, Y Katz, L Choshen, E Cohen-Karlik, N Liberman, A Toledo, ... arXiv preprint arXiv:1906.03897, 2019 | 44 | 2019 |
| SemEval-2019 task 1: Cross-lingual semantic parsing with UCCA D Hershcovich, Z Aizenbud, L Choshen, E Sulem, A Rappoport, O Abend arXiv preprint arXiv:1903.02953, 2019 | 43 | 2019 |
| The grammar-learning trajectories of neural language models L Choshen, G Hacohen, D Weinshall, O Abend arXiv preprint arXiv:2109.06096, 2021 | 41 | 2021 |
| Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification L Choshen, O Abend Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 40 | 2018 |
Omri AbendFaculty member, Hebrew University of JerusalemVerified email at mail.huji.ac.il