| Self-refine: Iterative refinement with self-feedback A Madaan, N Tandon, P Gupta, S Hallinan, L Gao, S Wiegreffe, U Alon, ... Advances in Neural Information Processing Systems 36, 2024 | 965 | 2024 |
| A dataset for movie description A Rohrbach, M Rohrbach, N Tandon, B Schiele Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 637 | 2015 |
| Movie description A Rohrbach, A Torabi, M Rohrbach, N Tandon, C Pal, H Larochelle, ... International Journal of Computer Vision 123, 94-120, 2017 | 416 | 2017 |
| Can AI language models replace human participants? D Dillion, N Tandon, Y Gu, K Gray Trends in Cognitive Sciences 27 (7), 597-600, 2023 | 270 | 2023 |
| Webchild: Harvesting and organizing commonsense knowledge from the web N Tandon, G De Melo, F Suchanek, G Weikum Proceedings of the 7th ACM international conference on Web search and data …, 2014 | 193 | 2014 |
| Tracking state changes in procedural text: a challenge dataset and models for process paragraph comprehension BD Mishra, L Huang, N Tandon, W Yih, P Clark arXiv preprint arXiv:1805.06975, 2018 | 154 | 2018 |
| From ‘F’to ‘A’on the NY regents science exams: An overview of the aristo project P Clark, O Etzioni, T Khot, D Khashabi, B Mishra, K Richardson, ... Ai Magazine 41 (4), 39-53, 2020 | 122 | 2020 |
| Webchild 2.0: Fine-grained commonsense knowledge distillation N Tandon, G De Melo, G Weikum Proceedings of ACL 2017, System Demonstrations, 115-120, 2017 | 115 | 2017 |
| Memory-assisted prompt editing to improve GPT-3 after deployment A Madaan, N Tandon, P Clark, Y Yang arXiv preprint arXiv:2201.06009, 2022 | 112 | 2022 |
| Learning language-visual embedding for movie understanding with natural-language A Torabi, N Tandon, L Sigal arXiv preprint arXiv:1609.08124, 2016 | 112 | 2016 |
| Commonsense knowledge in machine intelligence N Tandon, AS Varde, G de Melo ACM SIGMOD Record 46 (4), 49-52, 2018 | 109 | 2018 |
| Reasoning about actions and state changes by injecting commonsense knowledge N Tandon, BD Mishra, J Grus, W Yih, A Bosselut, P Clark arXiv preprint arXiv:1808.10012, 2018 | 95 | 2018 |
| Wiqa: A dataset for" what if..." reasoning over procedural text N Tandon, BD Mishra, K Sakaguchi, A Bosselut, P Clark arXiv preprint arXiv:1909.04739, 2019 | 94 | 2019 |
| Rl4f: Generating natural language feedback with reinforcement learning for repairing model outputs AF Akyürek, E Akyürek, A Madaan, A Kalyan, P Clark, D Wijaya, ... arXiv preprint arXiv:2305.08844, 2023 | 77 | 2023 |
| Acquiring comparative commonsense knowledge from the web N Tandon, G Melo, G Weikum Proceedings of the AAAI conference on artificial intelligence 28 (1), 2014 | 73 | 2014 |
| Domain-targeted, high precision knowledge extraction BD Mishra, N Tandon, P Clark Transactions of the Association for Computational Linguistics 5, 233-246, 2017 | 72 | 2017 |
| proscript: Partially ordered scripts generation via pre-trained language models K Sakaguchi, C Bhagavatula, RL Bras, N Tandon, P Clark, Y Choi arXiv preprint arXiv:2104.08251, 2021 | 65 | 2021 |
| Knowlywood: Mining activity knowledge from hollywood narratives N Tandon, G De Melo, A De, G Weikum Proceedings of the 24th ACM International on Conference on Information and …, 2015 | 63 | 2015 |
| Distilling task knowledge from how-to communities CX Chu, N Tandon, G Weikum Proceedings of the 26th International Conference on World Wide Web, 805-814, 2017 | 59 | 2017 |
| Everything happens for a reason: Discovering the purpose of actions in procedural text B Dalvi, N Tandon, A Bosselut, W Yih, P Clark Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 52 | 2019 |
