Accelerated Preference Elicitation with LLM-Based Proxies

Understand

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.