Summary of Openreviewer: a Specialized Large Language Model For Generating Critical Scientific Paper Reviews, by Maximilian Idahl et al.
OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews
by Maximilian Idahl, Zahra Ahmadi
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces OpenReviewer, a novel approach to generating high-quality peer reviews for machine learning and AI conference papers. The system relies on Llama-OpenReviewer-8B, an 8 billion parameter language model fine-tuned on 79,000 expert reviews from top conferences. OpenReviewer can process PDF paper submissions and review templates, extracting full text including technical content like equations and tables, before generating a structured review following conference-specific guidelines. The authors evaluate OpenReviewer’s performance on 400 test papers, finding that it produces more critical and realistic reviews compared to general-purpose language models like GPT-4 and Claude-3.5. While these other models tend to provide overly positive assessments, OpenReviewer’s recommendations closely match the distribution of human reviewer ratings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help writers improve their papers before they’re reviewed by others. It’s called OpenReviewer, and it uses special computer programs to read and understand papers very well. These programs can even add tables and equations to the review! The people who made OpenReviewer tested it on 400 different papers and found that it gave much better reviews than some other similar tools. It told authors what was good and what needed improvement, just like a real reviewer would. This is important because it helps make sure papers are really well-written before they’re shared with others. |
Keywords
» Artificial intelligence » Claude » Gpt » Language model » Llama » Machine learning
