Summary of Ai-driven Review Systems: Evaluating Llms in Scalable and Bias-aware Academic Reviews, by Keith Tyser et al.
AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews
by Keith Tyser, Ben Segev, Gaston Longhitano, Xin-Yu Zhang, Zachary Meeks, Jason Lee, Uday Garg, Nicholas Belsten, Avi Shporer, Madeleine Udell, Dov Te’eni, Iddo Drori
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Automatic reviewing systems are increasingly being used to analyze papers, provide early feedback, and reduce bias in the peer-review process. In this paper, we evaluate the alignment of automatic reviews with human reviews by comparing preferences between humans and large language models (LLMs). We also fine-tune an LLM to predict human preferences and identify limitations by introducing errors into papers and analyzing LLM responses. Additionally, we explore methods such as adaptive review questions, meta prompting, role-playing, and visual analysis to improve the reviewing process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automatic reviewers are helping with a lot of paper reviews, giving early feedback, making sure everything is fair, and looking at trends. Scientists looked at how well automatic reviews match human reviews by comparing what humans like best. They also used big computers to help figure out what humans will prefer when reading reviews. The team made some mistakes on purpose in papers to see where the computer reviewers go wrong. They also came up with new ways to ask questions, make it more fun, and look at pictures too! This helps make the review process better. |
Keywords
» Artificial intelligence » Alignment » Prompting
