用迭代框架让大模型自动修正电商搜索错词，提升匹配准确率。
IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System
- 基于RAG和思维链，结合领域与现实知识生成改写查询。
- 通过在线信号持续更新正样本，实现自纠错迭代优化。
- 已在美团配送系统落地，显著提升用户搜索体验。
现代电商平台搜索系统的核心在于将用户意图与候选商品精准匹配。然而，用户输入常因模糊或拼写错误导致搜索不准确。查询改写可通过修改或扩展查询来缓解此问题。传统方法依赖静态手工构建的改写词表，缺乏与电商领域知识及现实世界常识的交互。本文提出迭代框架IterQR，利用大语言模型（LLM）能力，每轮迭代包含三个阶段：基于检索增强生成（RAG）与思维链（CoT）的改写生成；通过自动正向改写更新收集在线信号；采用多任务目标对LLM进行后训练以生成新改写。该框架融合领域与真实世界知识，实现改写的自动更新与自我修正。IterQR已部署于中国领先的外卖平台美团配送的搜索系统中，服务用户并取得显著效果。
原文摘要 · Abstract (English)
The essence of modern e-Commercial search system lies in matching user's intent and available candidates depending on user's query, providing personalized and precise service. However, user's query may be incorrect due to ambiguous input and typo, leading to inaccurate search. These cases may be released by query rewrite: modify query to other representation or expansion. However, traditional query rewrite replies on static rewrite vocabulary, which is manually established meanwhile lacks interaction with both domain knowledge in e-Commercial system and common knowledge in the real world. In this paper, with the ability to generate text content of Large Language Models (LLMs), we provide an iterative framework to generate query rewrite. The framework incorporates a 3-stage procedure in each iteration: Rewrite Generation with domain knowledge by Retrieval-Augmented Generation (RAG) and query understanding by Chain-of-Thoughts (CoT); Online Signal Collection with automatic positive rewrite update; Post-training of LLM with multi task objective to generate new rewrites. Our work (named as IterQR) provides a comprehensive framework to generate \textbf{Q}uery \textbf{R}ewrite with both domain / real-world knowledge. It automatically update and self-correct the rewrites during \textbf{iter}ations. \method{} has been deployed in Meituan Delivery's search system (China's leading food delivery platform), providing service for users with significant improvement.
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