| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 5061 | 2021 |
| COMET: Commonsense transformers for automatic knowledge graph construction A Bosselut, H Rashkin, M Sap, C Malaviya, A Celikyilmaz, Y Choi ACL 2019, 2019 | 1167 | 2019 |
| QA-GNN: Reasoning with language models and knowledge graphs for question answering M Yasunaga, H Ren, A Bosselut, P Liang, J Leskovec NAACL 2021, 2021 | 640 | 2021 |
| Fast model editing at scale E Mitchell, C Lin, A Bosselut, C Finn, CD Manning ICLR 2022, 2022 | 447 | 2022 |
| (comet-) atomic 2020: On symbolic and neural commonsense knowledge graphs JD Hwang, C Bhagavatula, R Le Bras, J Da, K Sakaguchi, A Bosselut, ... AAAI 2021, 2021 | 446 | 2021 |
| Deep communicating agents for abstractive summarization A Celikyilmaz, A Bosselut, X He, Y Choi NAACL 2018, 2018 | 434 | 2018 |
| Memory-based model editing at scale E Mitchell, C Lin, A Bosselut, CD Manning, C Finn ICML 2022, 2022 | 306 | 2022 |
| Learning to write with cooperative discriminators A Holtzman, J Buys, M Forbes, A Bosselut, D Golub, Y Choi ACL 2018, 2018 | 292 | 2018 |
| GreaseLM: Graph reasoning enhanced language models for question answering X Zhang, A Bosselut, M Yasunaga, H Ren, P Liang, CD Manning, ... ICLR 2022, 2022 | 286 | 2022 |
| Meditron-70b: Scaling medical pretraining for large language models Z Chen, AH Cano, A Romanou, A Bonnet, K Matoba, F Salvi, ... arXiv preprint arXiv:2311.16079, 2023 | 247 | 2023 |
| Deep bidirectional language-knowledge graph pretraining M Yasunaga, A Bosselut, H Ren, X Zhang, CD Manning, PS Liang, ... NeurIPS 2022, 2022 | 234 | 2022 |
| Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering A Bosselut, R Le Bras, Y Choi AAAI 2021, 2021 | 186 | 2021 |
| The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 165 | 2021 |
| Commonsense knowledge base completion with structural and semantic context C Malaviya, C Bhagavatula, A Bosselut, Y Choi AAAI 2020, 2020 | 152 | 2020 |
| Counterfactual story reasoning and generation L Qin, A Bosselut, A Holtzman, C Bhagavatula, E Clark, Y Choi EMNLP 2019, 2019 | 150 | 2019 |
| Refiner: Reasoning feedback on intermediate representations D Paul, M Ismayilzada, M Peyrard, B Borges, A Bosselut, R West, ... EACL 2024, 2024 | 144 | 2024 |
| Simulating action dynamics with neural process networks A Bosselut, O Levy, A Holtzman, C Ennis, D Fox, Y Choi ICLR 2018, 2017 | 139 | 2017 |
| Commonsense reasoning for natural language processing M Sap, V Shwartz, A Bosselut, Y Choi, D Roth ACL 2020 Tutorial Abstracts, 2020 | 117 | 2020 |
| Modeling naive psychology of characters in simple commonsense stories H Rashkin, A Bosselut, M Sap, K Knight, Y Choi ACL 2018, 2018 | 111 | 2018 |
| Reasoning about actions and state changes by injecting commonsense knowledge N Tandon, BD Mishra, J Grus, W Yih, A Bosselut, P Clark EMNLP 2018, 2018 | 98 | 2018 |
Yejin ChoiStanford University / NVIDIA確認したメール アドレス: stanford.edu