Reproduce “LaMP-Val: Large Language Models Empower Personalized Valuation in Auction” on OpenResearch

About this paper

Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu and 7 more

LaMP-Val introduces a framework leveraging Large Language Models to personalize item valuation in auctions, addressing the oversight of individual preferences. The approach achieves a Weighted F1 score of 0.9084 for preference prediction and reduces Mean Absolute Error in valuation to 536, leading to significantly higher personalized utility and value for bidders in simulated auction scenarios.

Key insights

• Large Language Models can effectively integrate sophisticated semantic analysis to capture fine-grained, personalized preferences, addressing a long-standing gap in auction research.
• Custom evaluation metrics like Personalized Utility (PU) and Personalized Value (PV) are essential for accurately reflecting bidder satisfaction and effective strategy in personalized auction scenarios, beyond traditional economic gains.
• LLM-driven data augmentation combined with diversity instruction templating provides a robust method for creating specialized datasets and improving LLM performance for complex, nuanced tasks, while allowing for local deployment to address privacy concerns.

Summary by alphaXiv.