The rapid advancement of large language models has reshaped user search cognition, driving a paradigm shift from discrete keyword-based search to high-dimensional conversational interaction. However, existing e-commerce search architectures face a critical capability deficit in adapting to this change. Users are often caught in a dilemma: precise natural language descriptions frequently trigger zero-result scenarios, while the forced simplification of queries leads to decision overload from noisy, generic results. To tackle this challenge, we propose LEAPS (LLM-Empowered Adaptive Plugin for Taobao AI Search), which seamlessly upgrades traditional search systems via a "Broaden-and-Refine" paradigm. Specifically, it attaches plugins to both ends of the search pipeline: (1) Upstream, a Query Expander acts as an intent translator. It employs a novel three-stage training strategy--inverse data augmentation, posterior-knowledge supervised fine-tuning, and diversity-aware reinforcement learning--to generate adaptive and complementary query combinations that maximize the candidate product set. (2) Downstream, a Relevance Verifier serves as a semantic gatekeeper. By synthesizing multi-source data (e.g., OCR text, reviews) and leveraging chain-of-thought reasoning, it precisely filters noise to resolve selection overload. Extensive offline experiments and online A/B testing demonstrate that LEAPS significantly enhances conversational search experiences. Crucially, its non-invasive architecture preserves established retrieval performance optimized for short-text queries, while simultaneously allowing for low-cost integration into diverse back-ends. Fully deployed on Taobao AI Search since August 2025, LEAPS currently serves hundreds of millions of users monthly.

翻译：大型语言模型的快速发展重塑了用户的搜索认知，推动了从基于离散关键词的搜索范式向高维对话式交互的转变。然而，现有的电商搜索架构在适应这一变化方面面临关键的能力缺失。用户常常陷入两难困境：精确的自然语言描述频繁触发零结果场景，而被迫简化查询则导致来自嘈杂、通用结果的决策过载。为应对这一挑战，我们提出了LEAPS（面向淘宝AI搜索的LLM赋能自适应插件），它通过“扩展-精化”范式无缝升级传统搜索系统。具体而言，它在搜索流程的两端附加插件：（1）在上游，查询扩展器充当意图翻译器。它采用一种新颖的三阶段训练策略——逆向数据增强、后验知识监督微调以及多样性感知强化学习——来生成自适应的、互补的查询组合，以最大化候选商品集。（2）在下游，相关性验证器充当语义守门员。通过综合多源数据（例如OCR文本、评论）并利用思维链推理，它精确过滤噪声以解决选择过载问题。大量的离线实验和在线A/B测试表明，LEAPS显著提升了对话式搜索体验。至关重要的是，其非侵入式架构保留了为短文本查询优化的既定检索性能，同时允许以低成本集成到多样化的后端系统中。自2025年8月起在淘宝AI搜索全面部署，LEAPS目前每月为数亿用户提供服务。