| Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change) K Valmeekam, A Olmo, S Sreedharan, S Kambhampati arXiv preprint arXiv:2206.10498, 2022 | 231 | 2022 |
| On the Planning Abilities of Large Language Models--A Critical Investigation K Valmeekam, M Marquez, S Sreedharan, S Kambhampati Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 101 | 2023 |
| Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning L Guan, K Valmeekam, S Sreedharan, S Kambhampati Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 67 | 2023 |
| PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change K Valmeekam, M Marquez, A Olmo, S Sreedharan, S Kambhampati Thirty-seventh Conference on Neural Information Processing Systems Datasets …, 2023 | 63 | 2023 |
| On the planning abilities of large language models (a critical investigation with a proposed benchmark) K Valmeekam, S Sreedharan, M Marquez, A Olmo, S Kambhampati arXiv preprint arXiv:2302.06706, 2023 | 49 | 2023 |
| Can large language models really improve by self-critiquing their own plans? K Valmeekam, M Marquez, S Kambhampati arXiv preprint arXiv:2310.08118, 2023 | 38 | 2023 |
| LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks S Kambhampati, K Valmeekam, L Guan, K Stechly, M Verma, S Bhambri, ... arXiv preprint arXiv:2402.01817, 2024 | 23 | 2024 |
| Opinion Mining on Emojis using Deep Learning Techniques V Karthik, D Nair, J Anuradha Procedia Computer Science 132, 167-173, 2018 | 20 | 2018 |
| On the self-verification limitations of large language models on reasoning and planning tasks K Stechly, K Valmeekam, S Kambhampati arXiv preprint arXiv:2402.08115, 2024 | 12 | 2024 |
| RADAR-X: An interactive interface pairing contrastive explanations with revised plan suggestions V Karthik, S Sreedharan, S Sengupta, S Kambhampati Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 16051 …, 2021 | 12 | 2021 |
| RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions K Valmeekam, S Sreedharan, S Sengupta, S Kambhampati Proceedings of the International Conference on Automated Planning and …, 2022 | 10 | 2022 |
| Chain of thoughtlessness: An analysis of cot in planning K Stechly, K Valmeekam, S Kambhampati arXiv preprint arXiv:2405.04776, 2024 | 7 | 2024 |
| Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences L Guan, K Valmeekam, S Kambhampati The Eleventh International Conference on Learning Representations, 2022 | 6 | 2022 |
| On the role of large language models in planning, July 2023. Tutorial presented at the International Conference on Automated Planning and Scheduling (ICAPS), Prague S Kambhampati, K Valmeekam, M Marquez, L Guan | 4 | 2023 |
| Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning A Gundawar, M Verma, L Guan, K Valmeekam, S Bhambri, ... arXiv preprint arXiv:2405.20625, 2024 | 3 | 2024 |
| A Study of Explainable Decision Support for Longitudinal Sequential Decision Making K Valmeekam Arizona State University, 2021 | | 2021 |
| LLM-Modulo Frameworks as Compound AI Architectures for Robust Planning S Kambhampati, K Valmeekam, L Guan, M Verma, SBKSL Saldyt, ... | | |
| Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks S Kambhampati, K Valmeekam, L Guan, M Verma, K Stechly, S Bhambri, ... Forty-first International Conference on Machine Learning, 0 | | |
Subbarao KambhampatiArizona State University在 asu.edu 的电子邮件经过验证
