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Position: Machine Learning Conferences Should Establish a “Refutations and Critiques” Track
Created by
Haebom
Author
Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch, Brando Miranda, Matthias Gerstgrasser, Susan Zhang, Andreas Haupt, Isha Gupta, Elyas Obbad, Jesse Dodge, Jessica Zosa Forde, Francesco Orabona, Sanmi Koyejo, David Donoho
Outline
This paper points out the problem that erroneous or erroneous studies are accepted by academic societies due to the rapid development of the machine learning (ML) field, and proposes the establishment of a "Refutations and Critiques (R&C)" track in academic societies as a solution to this problem. It argues that the R&C track will contribute to the creation of a self-correcting research ecosystem by supporting critical research on existing research. It discusses the track design, review principles, potential problems, etc., and presents a case of refuting a paper presented at ICLR 2025 oral presentation.
Takeaways, Limitations
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Takeaways:
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A novel system proposal to enhance the self-correcting process of machine learning research
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Contributes to improving the credibility of research by encouraging critical review of existing research
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Emphasize the importance of error correction and improvement of research ethics
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Expanding the role and strengthening the responsibility of the society
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Limitations:
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Absence of specific mechanisms and guidelines for effective operation of R&C tracks
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Need to secure objectivity and reliability of rebuttal research
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Possibility of research activities being discouraged due to excessive criticism
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Bias issues that may arise during the judging process of the R&C track
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Further research is needed to determine how effectively R&C tracks actually work.
