# Large Language Model based Long-tail Query Rewriting in Taobao Search

arXiv: https://arxiv.org/abs/2311.03758 · HTML: https://mlsanity.com/paper/2311.03758/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 30 of 62)

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- [TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval](https://mlsanity.com/paper/2502.20969/) (2025)
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- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models](https://mlsanity.com/paper/2404.08695/) (2024)
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- [EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models](https://mlsanity.com/paper/2505.18594/) (2025)
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- [MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://mlsanity.com/paper/2509.21391/) (2025)
- [LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification](https://mlsanity.com/paper/2503.07937/) (2025)
- [Contextualizing Search Queries In-Context Learning for Conversational Rewriting with LLMs](https://mlsanity.com/paper/2502.15009/) (2025)
- [IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System](https://mlsanity.com/paper/2504.05309/) (2025)
- [Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models](https://mlsanity.com/paper/2505.07105/) (2025)

## Abstract (arXiv)

In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue. Along this line, query rewriting, serving as an important technique to bridge the semantic gaps inherent in the semantic matching process, has attached wide attention from the industry and academia. However, existing query rewriting methods often struggle to effectively optimize long-tail queries and alleviate the phenomenon of "few-recall" caused by semantic gap. In this paper, we present BEQUE, a comprehensive framework that Bridges the sEmantic gap for long-tail QUEries. In detail, BEQUE comprises three stages: multi-instruction supervised fine tuning (SFT), offline feedback, and objective alignment. We first construct a rewriting dataset based on rejection sampling and auxiliary tasks mixing to fine-tune our large language model (LLM) in a supervised fashion. Subsequently, with the well-trained LLM, we employ beam search to generate multiple candidate rewrites, and feed them into Taobao offline system to obtain the partial order. Leveraging the partial order of rewrites, we introduce a contrastive learning method to highlight the distinctions between rewrites, and align the model with the Taobao online objectives. Offline experiments prove the effectiveness of our method in bridging semantic gap. Online A/B tests reveal that our method can significantly boost gross merchandise volume (GMV), number of transaction (#Trans) and unique visitor (UV) for long-tail queries. BEQUE has been deployed on Taobao, one of most popular online shopping platforms in China, since October 2023.