# IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System

arXiv: https://arxiv.org/abs/2504.05309 · HTML: https://mlsanity.com/paper/2504.05309/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Large Language Model based Long-tail Query Rewriting in Taobao Search](https://mlsanity.com/paper/2311.03758/) (2023)

## Built on by (most cited first, 4 of 4)

- [VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search](https://mlsanity.com/paper/2504.05321/) (2025)
- [CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms](https://mlsanity.com/paper/2510.10095/) (2025)
- [When & How to Write for Personalized Demand-aware Query Rewriting in Video Search](https://mlsanity.com/paper/2602.17667/) (2025)
- [WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search](https://mlsanity.com/paper/2610.16927/) (2026)

## Abstract (arXiv)

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 Query Rewrite with both domain / real-world knowledge. It automatically update and self-correct the rewrites during iterations. method has been deployed in Meituan Delivery's search system (China's leading food delivery platform), providing service for users with significant improvement.