| Augmented language models: a survey G Mialon, R Dessė, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ... arXiv preprint arXiv:2302.07842, 2023 | 411 | 2023 |
| Multi-reward reinforced summarization with saliency and entailment R Pasunuru, M Bansal arXiv preprint arXiv:1804.06451, 2018 | 255 | 2018 |
| Opt-iml: Scaling language model instruction meta learning through the lens of generalization S Iyer, XV Lin, R Pasunuru, T Mihaylov, D Simig, P Yu, K Shuster, T Wang, ... arXiv preprint arXiv:2212.12017, 2022 | 188* | 2022 |
| Soft layer-specific multi-task summarization with entailment and question generation H Guo, R Pasunuru, M Bansal arXiv preprint arXiv:1805.11004, 2018 | 166 | 2018 |
| Multi-task video captioning with video and entailment generation R Pasunuru, M Bansal arXiv preprint arXiv:1704.07489, 2017 | 142 | 2017 |
| Multi-source domain adaptation for text classification via distancenet-bandits H Guo, R Pasunuru, M Bansal Proceedings of the AAAI conference on artificial intelligence 34 (05), 7830-7838, 2020 | 136 | 2020 |
| Reinforced video captioning with entailment rewards R Pasunuru, M Bansal arXiv preprint arXiv:1708.02300, 2017 | 134 | 2017 |
| Efficient large scale language modeling with mixtures of experts M Artetxe, S Bhosale, N Goyal, T Mihaylov, M Ott, S Shleifer, XV Lin, J Du, ... arXiv preprint arXiv:2112.10684, 2021 | 93 | 2021 |
| Scaling autoregressive multi-modal models: Pretraining and instruction tuning L Yu, B Shi, R Pasunuru, B Muller, O Golovneva, T Wang, A Babu, B Tang, ... arXiv preprint arXiv:2309.02591 2 (3), 2023 | 90 | 2023 |
| Dynamic multi-level multi-task learning for sentence simplification H Guo, R Pasunuru, M Bansal arXiv preprint arXiv:1806.07304, 2018 | 89 | 2018 |
| Efficiently summarizing text and graph encodings of multi-document clusters R Pasunuru, M Liu, M Bansal, S Ravi, M Dreyer Proceedings of the 2021 Conference of the North American Chapter of the …, 2021 | 64 | 2021 |
| Crowdsourcing lightweight pyramids for manual summary evaluation O Shapira, D Gabay, Y Gao, H Ronen, R Pasunuru, M Bansal, ... arXiv preprint arXiv:1904.05929, 2019 | 63 | 2019 |
| Complementary explanations for effective in-context learning X Ye, S Iyer, A Celikyilmaz, V Stoyanov, G Durrett, R Pasunuru arXiv preprint arXiv:2211.13892, 2022 | 59 | 2022 |
| Continual and multi-task architecture search R Pasunuru, M Bansal arXiv preprint arXiv:1906.05226, 2019 | 55 | 2019 |
| Autosem: Automatic task selection and mixing in multi-task learning H Guo, R Pasunuru, M Bansal arXiv preprint arXiv:1904.04153, 2019 | 54 | 2019 |
| Data augmentation for abstractive query-focused multi-document summarization R Pasunuru, A Celikyilmaz, M Galley, C Xiong, Y Zhang, M Bansal, J Gao Proceedings of the AAAI Conference on Artificial Intelligence 35 (15), 13666 …, 2021 | 53 | 2021 |
| Few-shot learning with multilingual generative language models XV Lin, T Mihaylov, M Artetxe, T Wang, S Chen, D Simig, M Ott, N Goyal, ... Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 49 | 2022 |
| Few-shot learning with multilingual language models XV Lin, T Mihaylov, M Artetxe, T Wang, S Chen, D Simig, M Ott, N Goyal, ... arXiv preprint arXiv:2112.10668, 2021 | 48 | 2021 |
| Towards improving abstractive summarization via entailment generation R Pasunuru, H Guo, M Bansal Proceedings of the Workshop on New Frontiers in Summarization, 27-32, 2017 | 46 | 2017 |
| Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, and Zornitsa Kozareva. 2022. Improving In-Context Few-Shot Learning via Self-Supervised Training M Chen arXiv preprint arXiv:2205.01703, 2022 | 45 | 2022 |
Mohit BansalParker Distinguished Professor, Computer Science, UNC Chapel HillVerified email at cs.unc.edu
