Accelerated Preference Elicitation with LLM-Based Proxies
This addresses the problem of cognitively taxing communication for bidders in auctions, offering a domain-specific incremental improvement.
The paper tackles the challenge of bidders describing preferences in combinatorial auctions by proposing LLM-based proxy designs that use natural language to approximate preferences with limited communication, achieving approximately efficient outcomes with five times fewer queries than classical methods.
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.