LEAPS: An LLM-Empowered Adaptive Plugin in Taobao AI Search
For e-commerce search platforms, LEAPS provides a non-intrusive, low-cost upgrade to handle natural-language queries, reducing zero-result scenarios and improving result relevance.
LEAPS addresses zero-result and noisy result problems in e-commerce search caused by natural-language queries by introducing a 'Broaden-and-Refine' plugin paradigm with a Query Expander and Relevance Verifier. Online A/B testing shows significant improvement in conversational shopping experience, serving hundreds of millions of users monthly on Taobao.
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.