发表机构
University of Surrey; eBay; Birmingham City University（萨里大学; 易贝; 伯明翰城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用LLM生成结构化语义监督信号（属性、解释、中心性）增强产品搜索，实验表明该方法在ESCI上显著提升检索性能，且LLM主要辅助而非替代人工判断。
AI 中文摘要
电子商务搜索需要区分仅与查询相关的产品和直接满足用户购物意图的产品。我们用结构化的大语言模型生成的查询和产品属性以及人工验证的相关性、解释和中心性判断来增强查询-产品对，并使用简单的双编码器检索器和多层感知机重排序器评估这些信号。在增强的ESCI子集上，人工特征预言机达到0.9382的nDCG@10，而无需人工的已训练Q+P配置达到0.9258。人工信号的合成近似整体达到0.9150，但为困难、低性能的查询提供了显著增益。消融研究表明，预言机的大部分改进来自后期编辑的解释和注释者评论，而非标量中心性特征，这表明大语言模型最擅长揭示和近似结构化语义监督，而非直接替代人类判断。
英文摘要
E-commerce search requires distinguishing products that are merely related to a query from those that directly satisfy the user's shopping intent. We augment query-product pairs with structured LLM-generated query and product attributes and human-validated relevance, explanations, and centrality judgments, and evaluate these signals using a simple dual-encoder retriever and MLP re-ranker. On an augmented subset of ESCI, a human-feature oracle reaches $0.9382$ nDCG@10, while a human-free trained $Q+P$ configuration reaches $0.9258$. Synthetic approximations of the human signals reach $0.9150$ overall but provide substantial gains for difficult, low-performing queries. Ablations show that most of the oracle improvement comes from post-edited explanations and annotator comments rather than the scalar centrality feature, suggesting that LLMs are most useful for exposing and approximating structured semantic supervision rather than replacing human judgment directly.
Comments16 pages, 2 figures
