5 papers
ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making
Yitong Luo, Ziang Chen, Hou Hei Lam +4
Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
ToMPO: Training LLM Strategic Decision Making from a Multi-Agent Perspective
Yiwen Zhang, Ziang Chen, Fanqi Kong +2
Large Language Models (LLMs) have been used to make decisions in complex scenarios, where they need models to think deeply, reason logically, and decide wisely. Many existing studi…
Can LLMs Generate Reliable Test Case Generators? A Study on Competition-Level Programming Problems
Yuhan Cao, Zian Chen, Kun Quan +17
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, capable of tackling complex tasks during inference. However, the extent to which LLMs can…
ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making
Yitong Luo, Hou Hei Lam, Ziang Chen +2
Despite recent advances in artificial intelligence (AI), it poses challenges to ensure personalized decision-making in tasks that are not considered in training datasets. To addres…