| 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 | 18230* | 2018 |
| 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 | 6394 | 2023 |
| 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 | 5300 | 2023 |
| Reflexion: Language agents with verbal reinforcement learning N Shinn, F Cassano, A Gopinath, K Narasimhan, S Yao Advances in Neural Information Processing Systems 36, 8634-8652, 2023 | 3520* | 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 | 1716 | 2016 |
| 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 | 1472 | 2024 |
| 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 | 758 | 2022 |
| Swe-agent: Agent-computer interfaces enable automated software engineering J Yang, CE Jimenez, A Wettig, K Lieret, S Yao, K Narasimhan, O Press Advances in Neural Information Processing Systems 37, 50528-50652, 2024 | 719 | 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 | 529 | 2015 |
| Toxicity in chatgpt: Analyzing persona-assigned language models A Deshpande, V Murahari, T Rajpurohit, A Kalyan, K Narasimhan Findings of EMNLP, 2023 | 513 | 2023 |
| Cognitive architectures for language agents T Sumers, S Yao, KR Narasimhan, TL Griffiths Transactions on Machine Learning Research, 2023 | 499 | 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 | 418 | 2019 |
| Projection-Based Constrained Policy Optimization. TY Yang, J Rosca, K Narasimhan, PJ Ramadge International Conference on Learning Representations, 2020 | 378 | 2020 |
| -bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains S Yao, N Shinn, P Razavi, K Narasimhan arXiv preprint arXiv:2406.12045, 2024 | 224 | 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 | 202 | 2016 |
| InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback J Yang, A Prabhakar, K Narasimhan, S Yao Neural Information Processing Systems (Datasets and Benchmarks), 2023 | 181 | 2023 |
| Fireact: Toward language agent fine-tuning B Chen, C Shu, E Shareghi, N Collier, K Narasimhan, S Yao arXiv preprint arXiv:2310.05915, 2023 | 179 | 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 | 179 | 2020 |
| Reflexion: Language agents with verbal reinforcement learning, 2023 N Shinn, F Cassano, B Labash, A Gopinath, K Narasimhan, S Yao URL https://arxiv. org/abs/2303.11366 1, 2023 | 161 | 2023 |
| Self-Attention Networks Can Process Bounded Hierarchical Languages S Yao, B Peng, C Papadimitriou, K Narasimhan ACL, 2021 | 137 | 2021 |
Shunyu YaoPrinceton UniversityVerified email at princeton.edu
