Product search is a crucial component of modern e-commerce platforms, with billions of user queries every day. In product search systems, first-stage retrieval should achieve high recall while ensuring efficient online deployment. Sparse retrieval is particularly attractive in this context due to its interpretability and storage efficiency. However, sparse retrieval methods suffer from severe vocabulary mismatch issues, leading to suboptimal performance in product search scenarios. With their potential for semantic analysis, large language models (LLMs) offer a promising avenue for mitigating vocabulary mismatch issues and thereby improving retrieval quality. Directly applying LLMs to sparse retrieval in product search exposes two key challenges:(1)Queries and product titles are typically short and highly susceptible to LLM-induced hallucinations, such as generating irrelevant expansion terms or underweighting critical literal terms like brand names and model numbers;(2)The large vocabulary space of LLMs leads to difficulty in initializing training effectively, making it challenging to learn meaningful sparse representations in such ultra-high-dimensional spaces.To address these challenges, we propose PROSPER, a framework for PROduct search leveraging LLMs as SParsE Retrievers. PROSPER incorporates: (1)A literal residual network that alleviates hallucination in lexical expansion by reinforcing underweighted literal terms through a residual compensation mechanism; and (2)A lexical focusing window that facilitates effective training initialization via a coarse-to-fine sparsification strategy.Extensive offline and online experiments show that PROSPER significantly outperforms sparse baselines and achieves recall performance comparable to advanced dense retrievers, while also achieving revenue increments online.

翻译：产品搜索是现代电子商务平台的关键组成部分，每日处理数十亿用户查询。在产品搜索系统中，第一阶段检索需要在保证高效在线部署的同时实现高召回率。稀疏检索因其可解释性和存储效率在此场景下尤为吸引人。然而，稀疏检索方法存在严重的词汇不匹配问题，导致其在产品搜索场景中性能欠佳。大型语言模型（LLMs）凭借其语义分析潜力，为缓解词汇不匹配问题、从而提升检索质量提供了有前景的途径。将LLMs直接应用于产品搜索的稀疏检索面临两个关键挑战：（1）查询和产品标题通常较短，极易受LLM引发的幻觉影响，例如生成无关的扩展词或低估品牌名、型号等关键字面词的重要性；（2）LLMs庞大的词汇空间导致有效初始化训练困难，难以在此类超高维空间中学习有意义的稀疏表示。为应对这些挑战，我们提出了PROSPER框架，一种利用LLMs作为稀疏检索器的产品搜索框架。PROSPER包含：（1）字面残差网络，通过残差补偿机制强化被低估的字面词，以缓解词汇扩展中的幻觉问题；（2）词汇聚焦窗口，通过从粗到细的稀疏化策略促进有效的训练初始化。大量离线和在线实验表明，PROSPER显著优于稀疏基线方法，其召回性能可与先进的稠密检索器相媲美，同时在线实现了收入增长。