t RAG : Term-level Retrieval-Augmented Generation for Domain-Adaptive Retrieval (2025)

Abstract: Neural retrieval models have emerged as an effective tool for information retrieval, but their performance suffers when there is a domain shift between training and test data distributions. Recent work aims to construct pseudo-training data for the target domain by generating domain-adapted pseudo-queries using large language models (LLMs). However, we identifies that LLMs exhibit a “seen term bias” where the generated pseudo-queries fail to include relevant “unseen” terms as expected for domain adaptation purposes. To address this limitation, we propose to improve the term recall of unseen query terms, by using term-level Retrieval-Augmented Generation (tRAG). Specifically, unlike existing document-level RAG, we propose to generate domain-specific keywords from all documents in the corpus, including those unseen in any individual document. To filter hallucination, generated keywords are retrieved and reranked, leveraging relevance feedback from both retrievers and LLMs. Experiments on the BEIR benchmark show tRAG significantly improves recall for unseen terms by 10.6% and outperforms LLM and retrieval-augmented generation baselines on overall retrieval performance.

## introduction in information retrieval (ir), fine-tuning neural retrieval models @xcite has been effective, where an encoder learns to project each document d to a dense vector close to that of a relevant query q. for domains without (q, d) pairs available for fine-tuning, pseudo-query generation (pqg), such as gpl @xcite , has been proposed, where a pseudo-query q for d is generated using large language models (llms), as demonstrated in figure 1 @xcite . based on the assumption that d shares similar terms with q, pqg has shown strong performance improvements, as q from d also becomes similar to q. however, as we show in §3.1, existing pqg models struggle to generate terms that are not in d-i.e., out-of-document terms-despite their frequent appearance in real queries. for instance, in the nfcorpus dataset of the beir benchmark, nearly 90% of generated pseudo query terms are in-document. we refer to this issue as seen term bias. ## related work sharing the goal of predicting gold query terms, we compare two baseline approaches, standard llmbased query generation ( §2.1) and rag ( §2.2), differing in vocabulary referred to for the query term prediction. in addition to llms' internal knowledge, the former relies on in-document vocabulary and the latter additionally employs in-corpus vocabulary by retrieving related documents from the corpus. our method is distinguished in that we further include out-of-corpus terms ( §2.3). ## proposed method: trag the pseudo-query generation task aims to generate a relevant query q for each d in the target corpus, to construct a pseudo-training dataset for the target domain via the relevant q-d pairs. a key limitation of existing llm-based approaches is the seen term bias, where the generated q is biased to the seen terms in d, and unseen gold query terms are often excluded in q. to overcome this limitation, we refine the initially generated q by augmenting unseen key terms via rag. in the subsequent sections, we first discuss the seen term bias of pqg models ( §3.1) and the standard rag approach and its limitations, as our baseline, which employs raw documents as the retrieval unit ( §3.2). then, we present our solution, term-level rag, proposing keywords as a better alternative for the retrieval unit ( §3.3). finally, we describe our generation-then-ranking approach, which enhances the method's robustness to unseen terms ( §3.4). ## conclusion this paper studied how to generate pseudo-queries to better align with the target corpus in zero-shot retrieval. our first contribution is focusing on a notable limitation of generated queries, based on seen terms within documents, failing to include relevant "unseen" terms , as expected for generating queries for domain adaptation purposes. to address this limitation, we proposed trag, to optimize the term recall of unseen query terms. our experiments showed significant improvements on beir benchmark, validating the critical role of unseen terms in mitigating the domain shift in zero-shot ir.