用大模型代理加速拍卖中偏好获取，少问5倍问题就能高效决策。
Accelerated Preference Elicitation with LLM-Based Proxies
- 结合大模型与逻辑学习，用自然语言快速推断竞拍者偏好
- 仅需经典方法1/5的提问次数，即可达到近似最优分配结果
- 适合需要低沟通成本的智能拍卖系统设计者
组合拍卖中的竞标者在向拍卖方表达偏好时面临巨大挑战。传统偏好获取方法基于查询机制，受过拟合学习启发，通过代理接口逐步学习偏好以计算高效分配。尽管这些机制理论查询效率高，实际通信量仍可能过于繁重。本文提出一类基于大模型的代理设计，利用自然语言实现高效偏好获取。该机制融合大模型流水线与DNF可正确学习技术，在通信受限条件下快速逼近真实偏好。我们构建了支持自然语言交互的测试沙盒验证方法。实验表明，最优的LLM代理设计仅需经典方法约五分之一的查询次数，即可达成近似最优分配结果。
原文摘要 · Abstract (English)
Bidders in combinatorial auctions face significant challenges when describing their preferences to an auctioneer. Classical work on preference elicitation focuses on query-based techniques inspired from proper learning--often via proxies that interface between bidders and an auction mechanism--to incrementally learn bidder preferences as needed to compute efficient allocations. Although such elicitation mechanisms enjoy theoretical query efficiency, the amount of communication required may still be too cognitively taxing in practice. We propose a family of efficient LLM-based proxy designs for eliciting preferences from bidders using natural language. Our proposed mechanism combines LLM pipelines and DNF-proper-learning techniques to quickly approximate preferences when communication is limited. To validate our approach, we create a testing sandbox for elicitation mechanisms that communicate in natural language. In our experiments, our most promising LLM proxy design reaches approximately efficient outcomes with five times fewer queries than classical proper learning based elicitation mechanisms.
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