Accelerated Preference Elicitation with LLM-Based Proxies

TL;DR

This paper introduces LLM-based proxies for preference elicitation in combinatorial auctions, significantly reducing communication and query complexity while maintaining near-efficient outcomes.

Contribution

It presents a novel LLM-based proxy mechanism that combines natural language processing with proper learning techniques for efficient preference elicitation.

Findings

Achieves approximately efficient outcomes with five times fewer queries.

Reduces communication complexity in preference elicitation.

Validates approach in a custom testing sandbox.

Abstract

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.