Crafting the Path: Robust Query Rewriting for Information Retrieval

TL;DR

This paper introduces Crafting the Path, a structured query rewriting method that enhances information retrieval by reducing reliance on LLM internal knowledge and improving accuracy, especially in less familiar domains.

Contribution

The paper presents a novel three-step query rewriting approach that outperforms existing methods and is less dependent on LLM internal knowledge.

Findings

Outperforms previous rewriting methods in various domains.

Reduces factual inaccuracies in generated queries.

Enhances retrieval-augmented generation performance.

Abstract

Query rewriting aims to generate a new query that can complement the original query to improve the information retrieval system. Recent studies on query rewriting, such as query2doc, query2expand and querey2cot, rely on the internal knowledge of Large Language Models (LLMs) to generate a relevant passage to add information to the query. Nevertheless, the efficacy of these methodologies may markedly decline in instances where the requisite knowledge is not encapsulated within the model's intrinsic parameters. In this paper, we propose a novel structured query rewriting method called Crafting the Path tailored for retrieval systems. Crafting the Path involves a three-step process that crafts query-related information necessary for finding the passages to be searched in each step. Specifically, the Crafting the Path begins with Query Concept Comprehension, proceeds to Query Type Identification, and finally conducts Expected Answer Extraction. Experimental results show that our method outperforms previous rewriting methods, especially in less familiar domains for LLMs. We demonstrate that our method is less dependent on the internal parameter knowledge of the model and generates queries with fewer factual inaccuracies. Furthermore, we observe that \name{} demonstrates superior performance in the retrieval-augmented generation scenarios.