ComplLLM：通过微调LLM发现互补信号以支持决策
ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making
AI总结：
ComplLLM通过微调LLM利用互补信息提升决策支持，有效恢复互补信号并生成合理解释。
摘要：
多智能体决策管道在互补性成立时（即不同智能体带来独特信息以辅助最终决策）可以优于单智能体工作流。我们提出了ComplLLM，一种基于决策理论的后训练框架，通过将互补信息作为奖励来微调决策助手LLM，以输出补充现有智能体决策的信号。我们在合成和现实任务中验证了ComplLLM，涉及领域专家，展示了该方法如何恢复已知的互补信息，并产生合理解释的互补信号以支持下游决策者。
英文摘要：
Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to the table to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM using complementary information as reward to output signals that complement existing agent decisions. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information and produces plausible explanations of complementary signals to support downstream decision-makers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
