From the 1 of 9 linked papers with an AI index.
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What We are Missing in Multimodal LLM Evaluation?
Po-han Li, Shenghui Chen, Sandeep Chinchali +1
Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their capabilities have advanced ra…
Learning to Coordinate without Communication under Incomplete Information
Shenghui Chen, Shufang Zhu, Giuseppe De Giacomo +1
Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. While commun…
Evaluating Human Trust in LLM-Based Planners: A Preliminary Study
Shenghui Chen, Yunhao Yang, Kayla Boggess +3
Large Language Models (LLMs) are increasingly used for planning tasks, offering unique capabilities not found in classical planners such as generating explanations and iterative re…
Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent
Shenghui Chen, Ruihan Zhao, Sandeep Chinchali +1
Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under i…
Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication
Shenghui Chen, Daniel Fried, Ufuk Topcu
Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introd…