LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912233284435968 |
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| author | Yang, Yingrui Carlson, Parker Qiao, Yifan Xie, Wentai He, Shanxiu Yang, Tao |
| author_facet | Yang, Yingrui Carlson, Parker Qiao, Yifan Xie, Wentai He, Shanxiu Yang, Tao |
| contents | This paper studies fast fusion of dense retrieval and sparse lexical retrieval, and proposes a cluster-based selective dense retrieval method called CluSD guided by sparse lexical retrieval. CluSD takes a lightweight cluster-based approach and exploits the overlap of sparse retrieval results and embedding clusters in a two-stage selection process with an LSTM model to quickly identify relevant clusters while incurring limited extra memory space overhead. CluSD triggers partial dense retrieval and performs cluster-based block disk I/O if needed. This paper evaluates CluSD and compares it with several baselines for searching in-memory and on-disk MS MARCO and BEIR datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval Yang, Yingrui Carlson, Parker Qiao, Yifan Xie, Wentai He, Shanxiu Yang, Tao Information Retrieval This paper studies fast fusion of dense retrieval and sparse lexical retrieval, and proposes a cluster-based selective dense retrieval method called CluSD guided by sparse lexical retrieval. CluSD takes a lightweight cluster-based approach and exploits the overlap of sparse retrieval results and embedding clusters in a two-stage selection process with an LSTM model to quickly identify relevant clusters while incurring limited extra memory space overhead. CluSD triggers partial dense retrieval and performs cluster-based block disk I/O if needed. This paper evaluates CluSD and compares it with several baselines for searching in-memory and on-disk MS MARCO and BEIR datasets. |
| title | LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2502.10639 |