L a MP -Val: Large Language Models Empower Personalized Valuation in Auction (2025)

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.

## introduction an auction, covering valuation and bidding, is a crucial economic mechanism that helps determine the market value of commodities or services through competitive bidding @xcite @xcite . valuation is the process by which bidders assess an item's worth of an item based on their individual needs and the product description @xcite . bidding, on the other hand, refers to a competitive process in which participants try to win ownership of the items and maximize their profits by developing strategic algorithms that comply with specific auction rules @xcite . the rise of online exchange platforms has broadened the use of auctions beyond traditional advertising. nowadays, auctions are commonly utilized for selling data @xcite and second-hand goods @xcite @xcite . this rapid expansion underscores the need for improved methods that help users achieve fair value based on their individual needs. as in figure 1 (a), most auction studies @xcite @xcite @xcite focus on theoretical bidding strategies and mechanisms but overlook individual valuation, despite their theoretical robustness. however, our preliminary experiments (sec 4.1) indicates that 1% valuation errors result in approximately 10% utility losses, demonstrating that valuation errors significantly affect final utility. while existing studies @xcite @xcite have investigated feature-based personalized valuation, these efforts rely on expert-engineered features for training valuation models. such approaches face challenges in scenarios requiring fine-grained user preference modeling, particularly when processing unstructured user-generated content. these synthetic, feature-based methods inherently struggle with representation inadequacy when managing user-provided textual descriptions that go beyond predefined feature boundaries as shown in figure 1 @xcite . this limitation leads to valuation errors, which ultimately reduce the final profit. moreover, traditional advertising auction metrics, such as utility and value @xcite , tend to focus solely on economic gains. they often overlook the emotional factors that influence user decisions, like collectible value and sentimental attachment. to our knowledge, there is currently no systematic text-based method that addresses individual preferences for item valuation, nor are there established evaluation metrics that correspond to these preferences. the advanced capabilities of large language models (llms) in semantic comprehension and following instructions @xcite ; openai, 2023) make them promising for capturing personalized preferences, such as interpreting ambiguous descriptions (e.g., "retro style"). stateof-the-art closed-source models, such as gemini @xcite and o3 @xcite , demonstrate strong reasoning abilities. however, their dependency on api-based access necessitates data transmission to cloud servers, which poses significant risks to privacy. in personal auction scenarios, the potential misuse of sensitive user information, such as transaction histories, severely limits the use of closed-source models in contexts where privacy is a concern. therefore, local deployment is essential to meet the requirements for privacy preservation. while open-source models like deepseek r1 (deepseek-ai, 2025) show similar reasoning performance, their substantial computational resource requirements lead us to seek more lightweight and efficient models. central to this investigation is the crucial question: how can we use llms to model personalized preferences and achieve accurate product valuation? in this paper, we introduce a framework called large language models-powered personalized valuation (lamp-val) to address this problem. lamp-val consists of three main components: data, learning, and evaluation. the data module addresses the critical challenge of constructing a novel dataset for llm fine-tuning in personalized valuation modeling, a domain previously hindered by three fundamental limitations: the value-price paradox @xcite , preference distribution skewness @xcite , and rationale absence in economic decision traces @xcite . the learning component develops a diversity template to fine-tune llms that enables llms to model nuanced personal valuation patterns, overcoming traditional approaches' inability to model item valuation influenced by personalized preference. the evaluation module pioneers a closed-loop system where llm-generated personalized valuations dynamically interact with bidding strategies and market environments. to address the inadequacy of conventional metrics in personalized scenarios, we propose personalized utility (pu) and personalized value (pv) these metrics are designed to assess both the precision of users' personalized valuations and the accuracy of their decision-making regarding bidding intentions. extensive experiments demonstrate that our methods can achieve significant profits compared to baseline approaches. our codes are available at https://github.com/sunjie279/lamp-val . the following sections introduce the preliminary material, our proposed framework, present and analyze the empirical results, related works, and finally conclude the paper. ## preliminary the vickrey auction, a distinctive variant of the sealed-bid auction @xcite , is widely used due to its incentive-compatible design. this auction model has garnered significant interest from both academia @xcite @xcite and industry @xcite , as it encourages bidders to place honest bids based on their item's valuation, as the winning bidder pays the second-highest bid, effectively mitigating the "winner's curse" of potentially overpaying @xcite . we will now define the key notations related to personalized valuation. consider a user participating in an auction system with m items and a total budget constraint of b. let b m represent the remaining budget when valuing the m-th item. each item m is linked to a personalized preference signal s m , which captures relevant information and reviews about the item. the valuation model v processes these preference signals to jointly generate dual outputs: the total profits of the users, specifically their utility and value @xcite , are given by the following equations: the optimization objective integrates utility with preference modeling accuracy: ## method in this section, we present the lamp-val framework, which integrates data, learning, and evaluation for personalized valuation. section 3.1 introduces our innovative dataset through llm-driven data augmentation. building upon this foundation, section 3.2 details the learning paradigm that enables llms to capture personalized valuation and preference from users' needs. finally, section 3.3 presents the evaluation environment and the proposed personalized metrics specifically designed for this scenario. ## experiments this section begins with the preliminary experiment. then we present an overview of the experimental setup, including baseline methods, evaluation metrics, and implementation details. in addition, we demonstrate the experimental results and conduct an ablation study. the results of these experiments aim to address the following questions: this section addresses the above three questions in order. ## case study to further validate the efficacy of our proposed evaluation metrics, pu and pv, in comparison to the conventional utility metric, we conducted a case study using five randomly selected samples from our experimental data, as illustrated in figure 5 . this study examines a specific bidder's preferences for auction items under a fixed budget constraint of $600, necessitating prioritization due to the infeasibility of bidding on all items of interest. in the first scenario, despite achieving higher overall utility and value, the bidder fails to acquire the desired items, resulting in zero pu and pv scores. in contrast, the second scenario demonstrates that even with lower utility and total value, the bidder successfully secures items of interest, leading to higher pu and pv scores. these results suggest that in real-world auction settings, where bidders prioritize obtaining specific items, pu and pv metrics more accurately indicate success than traditional measures of utility and value. this case study highlights the relevance of pu and pv as more effective metrics for evaluating auction outcomes, particularly in scenarios where bidders' preferences and budget constraints critically shape their bidding strategies. by focusing on the acquisition of desired items, these metrics align more closely with bidder satisfaction, reflecting the true nature of auction where personal preferences significantly influence bidding decisions. ## related work llms meet auction. while recent works on integrating llms with auction have flourished, they mainly focus on general bidding strategies rather than personalized valuation modeling for individual participants. chen and yuan (2023) model strategic interactions via llms in budgetconstrained bidding scenarios. @xcite jointly generate text through wordby-word bidding with multi-agent llms. zhu and horton (2024) simulate auctions via llm bidding agents, revealing human-aligned behavioral patterns. @xcite analyze ad integration in llm summaries via layered auction-module architecture. hajiaghayi and lahaie (2024a) embed ads in llm outputs via retrieval-augmented auctions for efficiency-fairness balance. yin (2025) study signaling effects on strategic behavior using llm-simulated disclosure in multi-agent auctions. shah et al. (2025) ## conclusion this study focuses on the valuation challenge considering user semantic preference, including accurately capturing user preference and valuation, and gaining more profit. we propose the lamp-val, a learning-based framework containing three essential parts: data, learning, and evaluation. lamp-val builds a valuation dataset for preference learning, fine-tuning llms, and proposes personalized evaluation metrics. through comprehensive evaluations across diverse auction scenarios, our methodology has proven effective in delivering accurate valuations and reflecting user preferences, achieving significant profit gains compared to baseline models in real-world auctions.