| Improving Language Understanding by Generative Pre-Training (GPT) A Radford, K Narasimhan, T Salimans, I Sutskever https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language …, 2018 | 13237* | 2018 |
| Tree of thoughts: Deliberate problem solving with large language models S Yao, D Yu, J Zhao, I Shafran, TL Griffiths, Y Cao, K Narasimhan Neural Information Processing Systems (NeurIPS), 2023 | 2030 | 2023 |
| React: Synergizing reasoning and acting in language models S Yao, J Zhao, D Yu, N Du, I Shafran, K Narasimhan, Y Cao International Conference on Learning Representations (ICLR), 2023 | 2018 | 2023 |
| Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation TD Kulkarni, KR Narasimhan, A Saeedi, JB Tenenbaum Neural Information Processing Systems (NIPS), 2016 | 1487 | 2016 |
| Reflexion: Language agents with verbal reinforcement learning N Shinn, F Cassano, A Gopinath, K Narasimhan, S Yao Advances in Neural Information Processing Systems 36, 2024 | 1337* | 2024 |
| Language understanding for text-based games using deep reinforcement learning K Narasimhan, T Kulkarni, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2015 | 487 | 2015 |
| Webshop: Towards scalable real-world web interaction with grounded language agents S Yao, H Chen, J Yang, K Narasimhan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 327 | 2022 |
| Toxicity in chatgpt: Analyzing persona-assigned language models A Deshpande, V Murahari, T Rajpurohit, A Kalyan, K Narasimhan Findings of EMNLP, 2023 | 319 | 2023 |
| A generalized algorithm for multi-objective reinforcement learning and policy adaptation R Yang, X Sun, K Narasimhan Advances in Neural Information Processing Systems (NeurIPS), 2019 | 308 | 2019 |
| Projection-Based Constrained Policy Optimization. TY Yang, J Rosca, K Narasimhan, PJ Ramadge International Conference on Learning Representations, 2020 | 296 | 2020 |
| SWE-bench: Can Language Models Resolve Real-World GitHub Issues? CE Jimenez, J Yang, A Wettig, S Yao, K Pei, O Press, K Narasimhan International Conference on Learning Representations (ICLR), 2024 | 277 | 2024 |
| Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning K Narasimhan, A Yala, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2016 | 196 | 2016 |
| Cognitive architectures for language agents TR Sumers, S Yao, K Narasimhan, TL Griffiths arXiv preprint arXiv:2309.02427, 2023 | 191 | 2023 |
| Keep CALM and Explore: Language Models for Action Generation in Text-based Games S Yao, R Rao, M Hausknecht, K Narasimhan Empirical Methods in Natural Language Processing (EMNLP), 2020 | 131 | 2020 |
| Nonparametric Spherical Topic Modeling with Word Embeddings K Batmanghelich, A Saeedi, K Narasimhan, S Gershman Association for Computational Linguistics (ACL), 2016 | 129 | 2016 |
| sk_p: a neural program corrector for MOOCs Y Pu, K Narasimhan, A Solar-Lezama, R Barzilay Companion Proceedings of the 2016 ACM SIGPLAN International Conference on …, 2016 | 114 | 2016 |
| Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge N Locascio, K Narasimhan, E DeLeon, N Kushman, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2016 | 112 | 2016 |
| Swe-agent: Agent-computer interfaces enable automated software engineering J Yang, CE Jimenez, A Wettig, K Lieret, S Yao, K Narasimhan, O Press arXiv preprint arXiv:2405.15793, 2024 | 104 | 2024 |
| Grounding language for transfer in deep reinforcement learning K Narasimhan, R Barzilay, T Jaakkola Journal of Artificial Intelligence Research 63, 849-874, 2018 | 98 | 2018 |
| Self-Attention Networks Can Process Bounded Hierarchical Languages S Yao, B Peng, C Papadimitriou, K Narasimhan ACL, 2021 | 94 | 2021 |
Shunyu YaoPrinceton UniversityVerified email at princeton.edu
