Recent Advances in Generative Information Retrieval

Author:

Tang Yubao1ORCID,Zhang Ruqing2ORCID,Ren Zhaochun3ORCID,Guo Jiafeng1ORCID,de Rijke Maarten4ORCID

Affiliation:

1. CAS Key Lab of Network Data Science and Technology, ICT, CAS & University of Chinese Academy of Sciences, Beijing, China

2. University of Chinese Academy of Sciences, CAS Key Lab of Network Data Science and Technology, ICT, CAS, Beijing, China

3. Leiden University, Leiden, Netherlands

4. University of Amsterdam, Amsterdam, Netherlands

Funder

Lenovo-CAS Joint Lab Youth Scientist Project

FINDHR project

Project

the Strategic Priority Research Program of the CAS

National Key Research and Development Program of China

Project LESSEN

Publisher

ACM

Reference35 articles.

1. Gabriel Bénédict Ruqing Zhang and Donald Metzler. 2023. Gen-IR@ SIGIR 2023: The First Workshop on Generative Information Retrieval. In SIGIR. 3460--3463.

2. Gabriel Bénédict, Ruqing Zhang, Donald Metzler, Andrew Yates, Romain Deffayet, Philipp Hager, and Sami Jullien. 2024. Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023. SIGIR Forum, Vol. 57, 2, Article 13 (jan 2024), bibinfonumpages23 pages.

3. Michele Bevilacqua Giuseppe Ottaviano Patrick Lewis Wen-tau Yih Sebastian Riedel and Fabio Petroni. 2022. Autoregressive Search Engines: Generating Substrings as Document Identifiers. In NeurIPS. 31668--31683.

4. Jiangui Chen Ruqing Zhang Jiafeng Guo Maarten de Rijke Wei Chen Yixing Fan and Xueqi Cheng. 2023 b. Continual Learning for Generative Retrieval over Dynamic Corpora. In CIKM. 306--315.

5. Jiangui Chen Ruqing Zhang Jiafeng Guo Maarten de Rijke Yiqun Liu Yixing Fan and Xueqi Cheng. 2023 c. A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning. In SIGIR. 1448--1457.

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