A factorized framework combining an auction module and an LLM module to generate welfare-maximizing ad summaries in an incentive-compatible manner.
Our ML-powered combinatorial clock auction (ML-CCA) significantly outperforms the traditional combinatorial clock auction (CCA) in terms of efficiency, achieving up to 9% higher efficiency in a substantially reduced number of rounds.
Integrating multi-task learning into machine learning-powered iterative combinatorial auctions can improve efficiency by leveraging shared information across bidders with similar valuation functions.