W ord2 P assage: Word-level Importance Re-weighting for Query Expansion (2025)

Abstract: Retrieval-augmented generation (RAG) enhances the quality of LLM generation by providing relevant chunks, but retrieving accurately from external knowledge remains challenging due to missing contextually important words in query. We present Word2Passage, a novel approach that improves retrieval accuracy by optimizing word importance in query expansion. Our method generates references at word, sentence, and passage levels for query expansion, then determines word importance by considering both their reference level origin and characteristics derived from query types and corpus analysis. Specifically, our method assigns distinct importance scores to words based on whether they originate from word, sentence, or passage-level references. Extensive experiments demonstrate that Word2Passage outperforms existing methods across various datasets and LLM configurations, effectively enhancing both retrieval accuracy and generation quality. The code is publicly available at https://github.com/DISL-Lab/Word2Passage

## introduction the advent of large language models (llms) has significantly influenced the field of information retrieval (ir). one notable advancement in this domain is retrieval-augmented generation (rag) @xcite , which integrates retrievers with generative models. by leveraging external knowledge sources during response generation @xcite , rag effectively mitigates key challenges of llms, such as hallucination @xcite . within the evolving landscape of rag, query expansion has become a key technique for improving retrieval performance @xcite . it enhances retrieval by either reformulating the original query or generating pseudo passagesartificially created text that captures semantically relevant information. for instance, hyde @xcite leverages a llm to generate a pseudo passage, which serves as an enriched query containing contextually relevant words. query2doc @xcite improves retrieval by repeating the original query a fixed number of times alongside the pseudo passage. these studies highlight that generating pseudo passage helps augment highly relevant words, enhancing retrieval performance @xcite @xcite . building upon them, recent studies have focused on optimizing the integration of pseudo passages with the original query. specifically, mugi @xcite calculates query importance based on the lengths of both the query and generated pseudo passages, ensuring balanced integration and improved retrieval performance. despite advancements in query expansion, methods like hyde, query2doc, and mugi rely on passage-level, treating all words in a pseudo passage equally @xcite , failing to differentiate high-importance words that are crucial for retrieval. also, when determining importance, solely relying on frequency overemphasizes common words and overlooks rare but meaningful ones, leading to query drift (see appendix a). therefore, low-importance or misleading words lead to reduced retrieval effectiveness. this highlights the need to properly adjust word importance in query expansion @xcite . to address this, we propose a novel approach named word2passage, which introduces a wordlevel importance re-weighting for query expansion. it generates pseudo references at three different levels, forming a hierarchical structure that progresses from @xmath0. this hierarchical structure enables a gradual expansion, capturing the importance of query-relevant words more accurately. as illustrated in figure 1 , word2passage assesses the importance of individual words by finely adjusting them based on the varying significance of each level, while also incorporating query type and domain characteristics to enhance word importance estimation. specifically, word2passage performs through a three-step process: in particular, we reveal that the significance of each reference level depends on the query type, e.g., description and entity, rather than adhering a single standard (see our main contributions are as follows: (1) word2passage: we are the first to present a multiperspective query expansion method that prompts llm to generate word, sentence, and passage level references. this multi-level approach enables a more fine-grained analysis of word importance than existing passage-only methods. ## related work information retrieval (ir) information retrieval (ir) is a key component in rag, where retrieval effectiveness directly impacts generation quality. existing retrievers can be categorized into lexical-based (sparse) retrievers and embeddingbased (dense) retrievers. lexical-based retrievers, such as bm25 (e. @xcite , are efficient, interpretable, and robust to domain shifts, but struggle with semantic variations due to exact word matching. embedding-based retrievers, such as dpr @xcite , ance @xcite , overcome this limitation by capturing semantic similarity, but require large-scale training and are sensitive to domain shifts. to improve retrieval effectiveness, ensemble retrievers @xcite @xcite ) combine bm25's efficiency with dense retrieval's semantic capabilities, enhancing retrieval perfomance. despite these advances, bm25 remains widely used for its zero-shot performance, efficiency, and interpretability, but struggles with semantic variations due to exact word matching. to address this limitation, we propose word2passage, which enhances bm25 retrieval by enriching queries with semantically relevant words and re-weighting word importance. ## proposed method: word2passage in this section, we start with formulating the impact of importance re-weighting in query expansion on @xmath1, @xmath2, specifically within the bm25 framework. next, we outline the three key components of word2passage: expanding 𝑄, optimizing 𝐼 𝑡 , and re-weighting 𝑡 steps. ## experiments in this section, we conduct experiments for ir and qa tasks, the two main tasks of rag. ## conclusion we introduce word2passage, a word-level reweighting approach for query expansion. by generating multi-level references and optimizing wordlevel importance, word2passage enhances query expansion effectiveness and improves retrieval performance. experimental results demonstrate that word2passage consistently outperforms existing methods, including hyde, q2d, and mugi, across diverse datasets and llm backbones. furthermore, word2passage exhibits synergy when integrated with a dense retrieval approach. our analysis of ir-qa performance alignment and componentwise evaluation further validate the effectiveness of word2passage's approach.