| Think you have solved question answering? try arc, the ai2 reasoning challenge P Clark, I Cowhey, O Etzioni, T Khot, A Sabharwal, C Schoenick, O Tafjord arXiv preprint arXiv:1803.05457, 2018 | 3778 | 2018 |
| The CN2 induction algorithm P Clark, T Niblett Machine learning 3 (4), 261-283, 1989 | 3757 | 1989 |
| 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, 46534-46594, 2023 | 2991 | 2023 |
| Can a suit of armor conduct electricity? a new dataset for open book question answering T Mihaylov, P Clark, T Khot, A Sabharwal arXiv preprint arXiv:1809.02789, 2018 | 2109 | 2018 |
| Learn to explain: Multimodal reasoning via thought chains for science question answering P Lu, S Mishra, T Xia, L Qiu, KW Chang, SC Zhu, O Tafjord, P Clark, ... Advances in Neural Information Processing Systems 35, 2507-2521, 2022 | 1986 | 2022 |
| Rule induction with CN2: Some recent improvements P Clark, R Boswell European working session on learning, 151-163, 1991 | 1431 | 1991 |
| The Fifth PASCAL Recognizing Textual Entailment Challenge. L Bentivogli, P Clark, I Dagan, D Giampiccolo TAC 7 (8), 1, 2009 | 1009 | 2009 |
| Decomposed prompting: A modular approach for solving complex tasks T Khot, H Trivedi, M Finlayson, Y Fu, K Richardson, P Clark, A Sabharwal arXiv preprint arXiv:2210.02406, 2022 | 697 | 2022 |
| Scitail: A textual entailment dataset from science question answering T Khot, A Sabharwal, P Clark Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 594 | 2018 |
| Complexity-based prompting for multi-step reasoning Y Fu, H Peng, A Sabharwal, P Clark, T Khot arXiv preprint arXiv:2210.00720, 2022 | 572 | 2022 |
| Transformers as soft reasoners over language P Clark, O Tafjord, K Richardson arXiv preprint arXiv:2002.05867, 2020 | 437 | 2020 |
| Unifiedqa: Crossing format boundaries with a single qa system D Khashabi, S Min, T Khot, A Sabharwal, O Tafjord, P Clark, H Hajishirzi arXiv preprint arXiv:2005.00700, 2020 | 426 | 2020 |
| Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning P Lu, L Qiu, KW Chang, YN Wu, SC Zhu, T Rajpurohit, P Clark, A Kalyan arXiv preprint arXiv:2209.14610, 2022 | 399 | 2022 |
| Qasc: A dataset for question answering via sentence composition T Khot, P Clark, M Guerquin, P Jansen, A Sabharwal Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8082-8090, 2020 | 370 | 2020 |
| ProofWriter: Generating implications, proofs, and abductive statements over natural language O Tafjord, B Dalvi, P Clark Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 …, 2021 | 352 | 2021 |
| Modeling biological processes for reading comprehension J Berant, V Srikumar, PC Chen, A Vander Linden, B Harding, B Huang, ... Proceedings of the 2014 conference on empirical methods in natural language …, 2014 | 296 | 2014 |
| Semeval-2013 task 7: The joint student response analysis and 8th recognizing textual entailment challenge MO Dzikovska, R Nielsen, C Brew, C Leacock, D Giampiccolo, ... Second joint conference on lexical and computational semantics (* SEM …, 2013 | 271 | 2013 |
| Answer extraction as sequence tagging with tree edit distance X Yao, B Van Durme, C Callison-Burch, P Clark Proceedings of the 2013 conference of the North American chapter of the …, 2013 | 255 | 2013 |
| Induction in Noisy Domains. P Clark, T Niblett EWSL, 11-30, 1987 | 239 | 1987 |
| Explaining answers with entailment trees B Dalvi, P Jansen, O Tafjord, Z Xie, H Smith, L Pipatanangkura, P Clark arXiv preprint arXiv:2104.08661, 2021 | 229 | 2021 |
Ashish SabharwalAllen Institute for AI (AI2)Verified email at allenai.org
