Papers by Katia Sycara
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models (2023.findings-emnlp)
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Simon Stepputtis, Joseph Campbell, Yaqi Xie, Zhengyang Qi, Wenxin Zhang, Ruiyi Wang, Sanketh Rangreji, Charles Lewis, Katia Sycara
| Challenge: | Deception and persuasion play a critical role in long-horizon multi-party dialogues, especially when the interests, goals, and motivations of the participants are not aligned. |
| Approach: | They propose a game in which players must determine each other’s hidden identities to complete their team’s objective. |
| Outcome: | The proposed model can be used to determine the true player identities of six human players in a cooperative-competitive game. |
Theory of Mind for Multi-Agent Collaboration via Large Language Models (2023.emnlp-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive accomplishments in reasoning and planning, but their abilities in multi-agent collaborations remain unexplored. |
| Approach: | They propose to use explicit belief state representations to enhance task performance and the accuracy of ToM inferences for LLM-based agents. |
| Outcome: | The proposed model improves performance and accuracy of ToM inferences for LLM-based agents. |
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models (2024.findings-emnlp)
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Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Simon Stepputtis, Joseph Campbell, Katia Sycara
| Challenge: | Reward hacking is a problem in reinforcement learning where the ability to specify the desired behavior of a reward function is difficult. |
| Approach: | They propose to use feedback as a potential-based shaping function to solicit and apply feedback from large language models to improve convergence speed and policy returns. |
| Outcome: | The proposed method improves convergence speed and policy returns over baselines even with significant ranking errors and eliminates the need for complex post-processing of reward functions. |