用互补信息训练大模型，帮决策者发现独特有用的新信号。
ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making
- 基于决策理论，用互补信息作为奖励微调大模型
- 在真实和模拟任务中成功恢复已知互补信号
- 适合需要多视角决策支持的专家系统
多智能体决策流程在各智能体具备互补性时，表现优于单一智能体。我们提出 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，微调决策辅助大模型，使其输出能补充现有智能体决策的独特信号。我们在涉及领域专家的真实与合成任务中验证了该方法，结果表明其能有效恢复已知的互补信息，并生成合理可信的解释，为下游决策者提供支持。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。