Summary of Withdrarxiv: a Large-scale Dataset For Retraction Study, by Delip Rao et al.
WithdrarXiv: A Large-Scale Dataset for Retraction Study
by Delip Rao, Jonathan Young, Thomas Dietterich, Chris Callison-Burch
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Digital Libraries (cs.DL); 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(original content) | Medium Difficulty Summary The WithdrarXiv dataset is the first large-scale collection of withdrawn papers from arXiv, containing over 14,000 papers with associated retraction comments. The authors develop a comprehensive taxonomy of retraction reasons, identifying 10 distinct categories ranging from critical errors to policy violations. They demonstrate a simple yet highly accurate zero-shot automatic categorization of retraction reasons, achieving a weighted average F1-score of 0.96. Additionally, they release an enriched version, WithdrarXiv-SciFy, which includes scripts for parsed full-text PDFs, enabling research in scientific feasibility studies, claim verification, and automated theorem proving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Withdrawn papers from arXiv can help us understand why some research is retracted. Scientists have created a big dataset with over 14,000 withdrawn papers and their reasons for being taken down. They found that there are many different reasons why papers get retracted, like mistakes or not following rules. They also made a special tool to help identify the reasons for retraction, which can be useful for checking if research is correct. |
Keywords
» Artificial intelligence » F1 score » Zero shot
