Recent Advances in Generative Information Retrieval
Author:
Affiliation:
1. CAS Key Lab of Network Data Science and Technology, ICT, CAS & University of Chinese Academy of Sciences, Beijing, China
2. Shandong University, Qingdao, China
3. University of Amsterdam, Amsterdam, Netherlands
Funder
None
the National Natural Science Foundation of China
Void
the Hybrid Intelligence Center
LESSEN; the research program NWA ORC 2020/21
the Lenovo-CAS Joint Lab Youth Scientist Project
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589335.3641239
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. 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.
3. Jiangui Chen Ruqing Zhang Jiafeng Guo Maarten de Rijke Wei Chen Yixing Fan and Xueqi Cheng. 2023. Continual Learning for Generative Retrieval over Dynamic Corpora. In CIKM. 306--315.
4. Jiangui Chen Ruqing Zhang Jiafeng Guo Maarten de Rijke Yiqun Liu Yixing Fan and Xueqi Cheng. 2023. A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning. In SIGIR. 1448--1457.
5. Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, and Xueqi Cheng. 2022. GERE: Generative evidence retrieval for fact verification. In SIGIR. 2184--2189.
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