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Inf. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>Information retrieval aims to find information that meets users\u2019 needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, and so on, while they share the same schema to estimate the relationship between texts. It indicates that a good IR model can generalize to different tasks and domains. However, previous studies indicate that state-of-the-art neural information retrieval (NIR) models, e.g., pre-trained language models (PLMs) are hard to generalize. It is mainly because the end-to-end fine-tuning paradigm makes the model overemphasize task-specific signals and domain biases but loses the ability to capture generalized essential signals. To address this problem, we propose a novel NIR training framework named NIR-Prompt for retrieval and reranking stages based on the idea of decoupling signal capturing and combination. NIR-Prompt exploits Essential Matching Module (EMM) to capture the essential matching signals and gets the description of tasks by Matching Description Module (MDM). The description is used as task-adaptation information to combine the essential matching signals to adapt to different tasks. Experiments under in-domain multi-task, out-of-domain multi-task, and new task adaptation settings show that NIR-Prompt can improve the generalization of PLMs in NIR for both retrieval and reranking stages compared with baselines.<\/jats:p>","DOI":"10.1145\/3626092","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T08:24:09Z","timestamp":1696235049000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7157-3410","authenticated-orcid":false,"given":"Shicheng","family":"Xu","sequence":"first","affiliation":[{"name":"Data Intelligence System Research Center, Institute of Computing Technology, CAS; University of Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1161-8546","authenticated-orcid":false,"given":"Liang","family":"Pang","sequence":"additional","affiliation":[{"name":"Data Intelligence System Research Center, Institute of Computing Technology, CAS, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1204-4820","authenticated-orcid":false,"given":"Huawei","family":"Shen","sequence":"additional","affiliation":[{"name":"Data Intelligence System Research Center, Institute of Computing Technology, CAS; University of Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-8195","authenticated-orcid":false,"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, CAS; University of Chinese Academy of Sciences, China"}]}],"member":"320","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"242","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Allen-Zhu Zeyuan","year":"2019","unstructured":"Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. 2019. 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