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Inf. Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>Due to the extraordinarily large number of parameters, fine-tuning large language models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications. To avoid fine-tuning, we can alternatively treat a LLM as a black-box (i.e., freeze the parameters of the LLM) and augment it with a retrieval-augmented generation (RAG) system, namely black-box RAG. Recently, black-box RAG has achieved success in knowledge-intensive tasks and has gained much attention. Existing black-box RAG methods typically fine-tune the retriever to cater to LLMs\u2019 preferences and concatenate all the retrieved documents as the input, which suffers from two issues: (1) Ignorance of Factual Information. The LLM preferred documents may not contain the factual information for the given question, which can mislead the retriever and hurt the effectiveness of black-box RAG; (2) Waste of Tokens. Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG. To address these issues, this article proposes a novel black-box RAG framework which utilizes the factual information in the retrieval and reduces the number of tokens for augmentation, dubbed FIT-RAG. FIT-RAG utilizes the factual information by constructing a bi-label document scorer which takes the factual information and LLMs\u2019 preferences as labels respectively. Besides, it reduces the tokens by introducing a self-knowledge recognizer and a sub-document-level token reducer, which enables FIT-RAG to avoid unnecessary augmentation and reduce augmentation tokens as much as possible. FIT-RAG achieves both superior effectiveness and efficiency, which is validated by extensive experiments across three open-domain question-answering datasets: TriviaQA, NQ, and PopQA. FIT-RAG can improve the answering accuracy of Llama2-13B-Chat by 14.3% on TriviaQA, 19.9% on NQ and 27.5% on PopQA, respectively. Furthermore, it can save approximately half of the tokens on average across the three datasets.<\/jats:p>","DOI":"10.1145\/3676957","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T11:29:23Z","timestamp":1720524563000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["FIT-RAG: Black-Box RAG with Factual Information and Token Reduction"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0550-3072","authenticated-orcid":false,"given":"Yuren","family":"Mao","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4260-8068","authenticated-orcid":false,"given":"Xuemei","family":"Dong","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1951-7714","authenticated-orcid":false,"given":"Wenyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-8450","authenticated-orcid":false,"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6895-7007","authenticated-orcid":false,"given":"Bin","family":"Wei","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2674-1638","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Gongshang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"111","volume-title":"On-Line Learning in Neural Networks","author":"Almeida Lu\u00eds B.","year":"1998","unstructured":"Lu\u00eds B. 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