Institute of Computing Technology, Chinese Academy IR
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework | |
Xu, Shicheng1,2; Pang, Liang1; Shen, Huawei1,2; Cheng, Xueqi2,3 | |
2024-03-01 | |
发表期刊 | ACM TRANSACTIONS ON INFORMATION SYSTEMS |
ISSN | 1046-8188 |
卷号 | 42期号:2页码:32 |
摘要 | Information retrieval aims to find information that meets users' 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. |
关键词 | Neural information retrieval dense retrieval reranking prompt learning |
DOI | 10.1145/3626092 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2022YFB3103700] ; National Key R&D Program of China[2022YFB3103704] ; National Natural Science Foundation of China (NSFC)[62276248] ; Youth Innovation Promotion Association CAS[2023111] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems |
WOS记录号 | WOS:001152702600025 |
出版者 | ASSOC COMPUTING MACHINERY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/38368 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Pang, Liang |
作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Shicheng,Pang, Liang,Shen, Huawei,et al. NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2024,42(2):32. |
APA | Xu, Shicheng,Pang, Liang,Shen, Huawei,&Cheng, Xueqi.(2024).NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework.ACM TRANSACTIONS ON INFORMATION SYSTEMS,42(2),32. |
MLA | Xu, Shicheng,et al."NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework".ACM TRANSACTIONS ON INFORMATION SYSTEMS 42.2(2024):32. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论