Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks-Reference-Cited by-同舟云学术

Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks

Published:2024-05-13 Issue: Volume:2240 Page:1362-1373
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Xu Shicheng¹^ORCID,Pang Liang²^ORCID,Shen Huawei²^ORCID,Cheng Xueqi²^ORCID,Chua Tat-Seng³^ORCID

Affiliation:

1. CAS Key Laboratory of AI Security, Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

2. CAS Key Laboratory of AI Security, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

3. Sea-NExT Joint Lab, National University of Singapore, Singapore, Singapore

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Youth Innovation Promotion Association CAS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645363

Reference42 articles.

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3. Andrew Drozdov, Nathanael Sch"arli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2023. Compositional Semantic Parsing with Large Language Models. In The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=gJW8hSGBys8

4. Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, et al. 2018. T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples. In Proceedings of the 2018 Conference on LREC. European Language Resources Association (ELRA), Miyazaki, Japan. https://aclanthology.org/L18--1544