Listwise Generative Retrieval Models via a Sequential Learning Process

doi:10.1145/3653712

	Listwise Generative Retrieval Models via a Sequential Learning Process
	Tang, Yubao 1,2; Zhang, Ruqing 1,2; Guo, Jiafeng 1,2; De Rijke, Maarten 3; Chen, Wei 1,2; Cheng, Xueqi 1,2
	2024-09-01
发表期刊	ACM TRANSACTIONS ON INFORMATION SYSTEMS
ISSN	1046-8188
卷号	42 期号:5 页码:31
摘要	Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing GR models commonly employ maximum likelihood estimation (MLE) for optimization: This involves maximizing the likelihood of a single relevant docid given an input query, with the assumption that the likelihood for each docid is independent of the other docids in the list. We refer to these models as the pointwise approach in this article. While the pointwise approach has been shown to be effective in the context of GR, it is considered sub-optimal due to its disregard for the fundamental principle that ranking involves making predictions about lists. In this article, we address this limitation by introducing an alternative listwise approach, which empowers the GR model to optimize the relevance at the docid list level. Specifically, we view the generation of a ranked docid list as a sequence learning process: At each step, we learn a subset of parameters that maximizes the corresponding generation likelihood of the ith docid given the (preceding) top i - 1 docids. To formalize the sequence learning process, we design a positional conditional probability for GR. To alleviate the potential impact of beam search on the generation quality during inference, we perform relevance calibration on the generation likelihood of model-generated docids according to relevance grades. We conduct extensive experiments on representative binary and multi-graded relevance datasets. Our empirical results demonstrate that our method outperforms state-of-the-art GR baselines in terms of retrieval performance.
关键词	Document retrieval generative retrieval listwise approach
DOI	10.1145/3653712
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the CAS[XDB0680102] ; National Key Research and Development Program of China[2023YFA1011602] ; National Key Research and Development Program of China[JCKY2022130C039] ; Lenovo-CAS Joint Lab Youth Scientist Project ; CAS Project for Young Scientists in Basic Research[YSBR-034] ; Innovation Project of ICT CAS[E261090] ; Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research - Dutch Research Council (NWO)[NWA.1389.20.183] ; Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research - Dutch Research Council (NWO)[NWA ORC 2020/21] ; European Union's Horizon Europe research and innovation program[101070212]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001253867000021
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39847
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Guo, Jiafeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, 6 Kexueyuan South Rd, Beijing, Peoples R China 3.Univ Amsterdam, Amsterdam, Netherlands
推荐引用方式 GB/T 7714	Tang, Yubao,Zhang, Ruqing,Guo, Jiafeng,et al. Listwise Generative Retrieval Models via a Sequential Learning Process[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2024,42(5):31.
APA	Tang, Yubao,Zhang, Ruqing,Guo, Jiafeng,De Rijke, Maarten,Chen, Wei,&Cheng, Xueqi.(2024).Listwise Generative Retrieval Models via a Sequential Learning Process.ACM TRANSACTIONS ON INFORMATION SYSTEMS,42(5),31.
MLA	Tang, Yubao,et al."Listwise Generative Retrieval Models via a Sequential Learning Process".ACM TRANSACTIONS ON INFORMATION SYSTEMS 42.5(2024):31.