Summary of Are We There Yet? Revealing the Risks Of Utilizing Large Language Models in Scholarly Peer Review, by Rui Ye et al.
Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
by Rui Ye, Xianghe Pang, Jingyi Chai, Jiaao Chen, Zhenfei Yin, Zhen Xiang, Xiaowen Dong, Jing Shao, Siheng Chen
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in large language models (LLMs) have integrated them into scholarly peer review, showing promising results. However, this unchecked adoption poses significant risks to the integrity of the system. This study comprehensively analyzes the vulnerabilities of LLM-generated reviews by focusing on manipulation and inherent flaws. The experiments show that injecting covert content into manuscripts can manipulate LLM reviews, leading to inflated ratings and reduced alignment with human reviews. Simulations demonstrate that manipulating 5% of reviews could cause 12% of papers to lose their top-30% rankings. Additionally, LLMs exhibit implicit manipulation susceptibility compared to humans, and inherent flaws such as favoring well-known authors in single-blind review processes. This highlights the risks of over-reliance on LLMs in peer review, emphasizing the need for robust safeguards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that artificial intelligence (AI) helps with scientific reviewing correctly. Right now, AI is being used to help reviewers, but it’s not perfect and can be tricked into giving wrong reviews. The study shows how easy it is to manipulate AI-generated reviews by adding fake information or highlighting small mistakes in a paper. If this happens, papers might be ranked too high or low. Additionally, the AI system can favor well-known authors over new ones. This highlights the need for more careful use of AI in scientific reviewing. |
Keywords
» Artificial intelligence » Alignment
