Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering-Reference-Cited by-同舟云学术

Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering

Published:2024-05-13 Issue: Volume:33 Page:4372-4382
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Sun Hongda¹^ORCID,Liu Yuxuan²^ORCID,Wu Chengwei³^ORCID,Yan Haiyu⁴^ORCID,Tai Cheng⁵^ORCID,Gao Xin⁶^ORCID,Shang Shuo⁷^ORCID,Yan Rui¹^ORCID

Affiliation:

1. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China

2. Nankai University, Tianjin, China

3. Beijing Academy of Artificial Intelligence, Beijing, China

4. Renmin University of China, Beijing, China

5. Moqi, Inc., Singapore, Singapore

6. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

7. University of Electronic Science and Technology of China, Chengdu, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645670

Reference54 articles.

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3. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.

4. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023).

5. Reading Wikipedia to Answer Open-Domain Questions

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