Neural Retrievers are Biased Towards LLM-Generated Content

Author:

Dai Sunhao1ORCID,Zhou Yuqi1ORCID,Pang Liang2ORCID,Liu Weihao1ORCID,Hu Xiaolin1ORCID,Liu Yong1ORCID,Zhang Xiao1ORCID,Wang Gang3ORCID,Xu Jun1ORCID

Affiliation:

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

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

3. Noah's Ark Lab, Huawei, Shenzhen, China

Funder

Youth Innovation Promotion Association CAS

Beijing Natural Science Foundation

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

ACM

Reference76 articles.

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3. Ian L Alberts, Lorenzo Mercolli, Thomas Pyka, George Prenosil, Kuangyu Shi, Axel Rominger, and Ali Afshar-Oromieh. 2023. Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be? European journal of nuclear medicine and molecular imaging, Vol. 50, 6 (2023), 1549--1552.

4. Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard G Baraniuk. 2023. Self-consuming generative models go mad. arXiv preprint arXiv:2307.01850 (2023).

5. Kevin Aslett, Zeve Sanderson, William Godel, Nathaniel Persily, Jonathan Nagler, and Joshua A Tucker. 2023. Online searches to evaluate misinformation can increase its perceived veracity. Nature (2023), 1--9.

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