Generative Dense Retrieval: Memory Can Be a Burden
| Autor: | Yuan, Peiwen, Wang, Xinglin, Feng, Shaoxiong, Pan, Boyuan, Li, Yiwei, Wang, Heda, Miao, Xupeng, Li, Kan |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | EACL 2024 main |
| Druh dokumentu: | Working Paper |
| Popis: | Generative Retrieval (GR), autoregressively decoding relevant document identifiers given a query, has been shown to perform well under the setting of small-scale corpora. By memorizing the document corpus with model parameters, GR implicitly achieves deep interaction between query and document. However, such a memorizing mechanism faces three drawbacks: (1) Poor memory accuracy for fine-grained features of documents; (2) Memory confusion gets worse as the corpus size increases; (3) Huge memory update costs for new documents. To alleviate these problems, we propose the Generative Dense Retrieval (GDR) paradigm. Specifically, GDR first uses the limited memory volume to achieve inter-cluster matching from query to relevant document clusters. Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents. The coarse-to-fine process maximizes the advantages of GR's deep interaction and DR's scalability. Besides, we design a cluster identifier constructing strategy to facilitate corpus memory and a cluster-adaptive negative sampling strategy to enhance the intra-cluster mapping ability. Empirical results show that GDR obtains an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability. Comment: EACL 2024 main |
| Databáze: | arXiv |
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