LaMP-Val: Large Language Models Empower Personalized Valuation in Auction
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918176595378176 |
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| author | Sun, Jie Zhang, Tianyu Jiang, Houcheng Huang, Kexin Shu, Xiang Zhu, Zhibo Ma, Lintao Lu, Xingyu Zhou, Jun Wu, Junkang Luo, Chi Zhang, An Wu, Junkang Wu, Jiancan Wang, Xiang |
| author_facet | Sun, Jie Zhang, Tianyu Jiang, Houcheng Huang, Kexin Shu, Xiang Zhu, Zhibo Ma, Lintao Lu, Xingyu Zhou, Jun Wu, Junkang Luo, Chi Zhang, An Wu, Junkang Wu, Jiancan Wang, Xiang |
| contents | 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 \underline{La}nguage \underline{M}odels-powered \underline{P}ersonalized \underline{Val}uation (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LaMP-Val: Large Language Models Empower Personalized Valuation in Auction Sun, Jie Zhang, Tianyu Jiang, Houcheng Huang, Kexin Shu, Xiang Zhu, Zhibo Ma, Lintao Lu, Xingyu Zhou, Jun Wu, Junkang Luo, Chi Zhang, An Wu, Junkang Wu, Jiancan Wang, Xiang Computational Engineering, Finance, and Science 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 \underline{La}nguage \underline{M}odels-powered \underline{P}ersonalized \underline{Val}uation (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. |
| title | LaMP-Val: Large Language Models Empower Personalized Valuation in Auction |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.15817 |