用大模型动态扩展和过滤搜索词，提升淘宝搜索准确率
LEAPS: An LLM-Empowered Adaptive Plugin in Taobao AI Search
- 上游用大模型生成互补查询扩大候选集，下游用多源信息验证相关性
- 线上测试显示搜索结果相关性提升显著，零结果问题减少
- 无需改动原有系统，适合电商搜索场景快速接入
大语言模型的兴起使用户从关键词搜索转向自然语言、多约束查询，但现有电商搜索架构难以应对。用户面临两难：精确查询常无结果，简化查询则产生大量噪声。为此，我们提出LEAPS（Taobao AI搜索中的大模型赋能自适应插件），通过“广度扩展-精细筛选”范式，在搜索流程两端接入插件。（1）上游查询扩展器利用逆向数据增强、后验知识监督微调与多样性感知强化学习训练，生成自适应的互补查询组合以最大化候选集；（2）下游相关性验证器通过链式思维推理，融合OCR文本、评论等多源信号过滤噪声。离线实验与线上A/B测试表明，LEAPS显著改善对话式购物体验，且非侵入式架构保持原有短文本检索性能，支持低成本适配多种后端。自2025年8月起全量部署于淘宝AI搜索，每月服务数亿用户。
原文摘要 · Abstract (English)
The rapid rise of large language models has shifted user search behavior from discrete keywords to natural-language, multi-constraint queries--a shift existing e-commerce search architectures struggle to accommodate. Users face a dilemma: precise natural-language queries often trigger zero-result scenarios, while forced simplification yields noisy, generic results that overwhelm decision-making. To address this, we propose LEAPS (LLM-Empowered Adaptive Plugin in Taobao AI Search), which upgrades traditional search pipelines via a "Broaden-and-Refine" paradigm by attaching plugins at both ends. (1) Upstream, a Query Expander generates adaptive, complementary query combinations to maximize the candidate set, trained via a three-stage strategy of inverse data augmentation, posterior-knowledge supervised fine-tuning, and diversity-aware reinforcement learning. (2) Downstream, a Relevance Verifier filters noise by synthesizing multi-source signals (e.g., OCR text, reviews) with chain-of-thought reasoning. Extensive offline experiments and online A/B testing show that LEAPS significantly enhances the conversational shopping experience, while its non-intrusive architecture preserves established short-text retrieval performance and enables low-cost integration with diverse back-ends. Fully deployed on Taobao AI Search since August 2025, LEAPS serves hundreds of millions of users monthly.
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