@inproceedings{sun-etal-2025-lamp,
title = "{L}a{MP}-Val: Large Language Models Empower Personalized Valuation in Auction",
author = "Sun, Jie and
Zhang, Tianyu and
Jiang, Houcheng and
Huang, Kexin and
Shu, Xiang and
Zhu, Zhibo and
Ma, Lintao and
Lu, Xingyu and
Zhou, Jun and
Wu, Junkang and
Luo, Chi and
Zhang, An and
Wu, Jiancan and
Wang, Xiang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.31/",
pages = "579--595",
ISBN = "979-8-89176-335-7",
abstract = "Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users' unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large Language Models-powered Personalized Valuation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs' capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches."
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<abstract>Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users’ unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large Language Models-powered Personalized Valuation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs’ capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches.</abstract>
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%0 Conference Proceedings
%T LaMP-Val: Large Language Models Empower Personalized Valuation in Auction
%A Sun, Jie
%A Zhang, Tianyu
%A Jiang, Houcheng
%A Huang, Kexin
%A Shu, Xiang
%A Zhu, Zhibo
%A Ma, Lintao
%A Lu, Xingyu
%A Zhou, Jun
%A Wu, Junkang
%A Luo, Chi
%A Zhang, An
%A Wu, Jiancan
%A Wang, Xiang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F sun-etal-2025-lamp
%X Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users’ unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large Language Models-powered Personalized Valuation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs’ capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches.
%U https://aclanthology.org/2025.findings-emnlp.31/
%P 579-595
Markdown (Informal)
[LaMP-Val: Large Language Models Empower Personalized Valuation in Auction](https://aclanthology.org/2025.findings-emnlp.31/) (Sun et al., Findings 2025)
ACL
- Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, and Xiang Wang. 2025. LaMP-Val: Large Language Models Empower Personalized Valuation in Auction. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 579–595, Suzhou, China. Association for Computational Linguistics.