A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning-Reference-Cited by-同舟云学术

A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning

Published:2023-07-18 Issue: Volume: Page:
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Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Chen Jiangui¹^ORCID,Zhang Ruqing¹^ORCID,Guo Jiafeng¹^ORCID,de Rijke Maarten²^ORCID,Liu Yiqun³^ORCID,Fan Yixing¹^ORCID,Cheng Xueqi¹^ORCID

Affiliation:

1. ICT, CAS & University of Chinese Academy of Sciences, Beijing, China

2. University of Amsterdam, Amsterdam, Netherlands

3. Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China (NSFC)

Lenovo-CAS Joint Lab Youth Scientist Project

Youth Innovation Promotion Association CAS

Young Elite Scientist Sponsorship Program by CAST

Hybrid Intelligence Center

China Scholarship Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591631

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2. Michele Bevilacqua , Giuseppe Ottaviano , Patrick Lewis , Wen tau Yih , Sebastian Riedel, and Fabio Petroni. 2022 . Autoregressive Search Engines: Generating Substrings as Document Identifiers. In arXiv pre-print 2204.10629. Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen tau Yih, Sebastian Riedel, and Fabio Petroni. 2022. Autoregressive Search Engines: Generating Substrings as Document Identifiers. In arXiv pre-print 2204.10629.

3. Michael Burrows and David Wheeler. 1994. A Block-sorting Lossless Data Compression Algorithm. In Digital SRC Research Report. Citeseer. Michael Burrows and David Wheeler. 1994. A Block-sorting Lossless Data Compression Algorithm. In Digital SRC Research Report. Citeseer.

4. Reading Wikipedia to Answer Open-Domain Questions

5. GERE: Generative Evidence Retrieval for Fact Verification

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