Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?

• URL: http://arxiv.org/abs/2408.13430v1
• Date: Sat, 24 Aug 2024 01:51:23 GMT
• Title: Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?
• Authors: Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, Weijie J. Su,
• Abstract summary: We conducted an experiment during the 2023 International Conference on Machine Learning (ICML) We received 1,342 rankings, each from a distinct author, pertaining to 2,592 submissions. We focus on the Isotonic Mechanism, which calibrates raw review scores using author-provided rankings.
• Score: 52.00419656272129
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML) that requested authors with multiple submissions to rank their own papers based on perceived quality. We received 1,342 rankings, each from a distinct author, pertaining to 2,592 submissions. In this paper, we present an empirical analysis of how author-provided rankings could be leveraged to improve peer review processes at machine learning conferences. We focus on the Isotonic Mechanism, which calibrates raw review scores using author-provided rankings. Our analysis demonstrates that the ranking-calibrated scores outperform raw scores in estimating the ground truth ``expected review scores'' in both squared and absolute error metrics. Moreover, we propose several cautious, low-risk approaches to using the Isotonic Mechanism and author-provided rankings in peer review processes, including assisting senior area chairs' oversight of area chairs' recommendations, supporting the selection of paper awards, and guiding the recruitment of emergency reviewers. We conclude the paper by addressing the study's limitations and proposing future research directions.

Related papers

• Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment [59.09144776166979]
  Large language models (LLMs) represent a promising, but controversial, tool in aiding scientific peer review. This study evaluates the usefulness of LLMs in a conference setting as a tool for vetting paper submissions against submission standards.
  arXiv  Detail & Related papers  (2024-11-05T18:58:00Z)
• Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews [51.453135368388686]
  We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM) Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level.
  arXiv  Detail & Related papers  (2024-03-11T21:51:39Z)
• Eliciting Honest Information From Authors Using Sequential Review [13.424398627546788]
  We propose a sequential review mechanism that can truthfully elicit the ranking information from authors. The key idea is to review the papers of an author in a sequence based on the provided ranking and conditioning the review of the next paper on the review scores of the previous papers.
  arXiv  Detail & Related papers  (2023-11-24T17:27:39Z)
• The Isotonic Mechanism for Exponential Family Estimation [31.542906034919977]
  In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we aim to employ these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences. This mechanism generates adjusted scores that closely align with the original scores while adhering to author-specified rankings.
  arXiv  Detail & Related papers  (2023-04-21T17:59:08Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Integrating Rankings into Quantized Scores in Peer Review [61.27794774537103]
  In peer review, reviewers are usually asked to provide scores for the papers. To mitigate this issue, conferences have started to ask reviewers to additionally provide a ranking of the papers they have reviewed. There are no standard procedure for using this ranking information and Area Chairs may use it in different ways. We take a principled approach to integrate the ranking information into the scores.
  arXiv  Detail & Related papers  (2022-04-05T19:39:13Z)
• Ranking Scientific Papers Using Preference Learning [48.78161994501516]
  We cast it as a paper ranking problem based on peer review texts and reviewer scores. We introduce a novel, multi-faceted generic evaluation framework for making final decisions based on peer reviews.
  arXiv  Detail & Related papers  (2021-09-02T19:41:47Z)
• Analyzing the Machine Learning Conference Review Process [41.049292105761246]
  We critically analyze the review process through a comprehensive study of papers submitted to ICLR between 2017 and 2020. Our findings suggest strong institutional bias in accept/reject decisions, even after controlling for paper quality. We find evidence for a gender gap, with female authors receiving lower scores, lower acceptance rates, and fewer citations per paper than their male counterparts.
  arXiv  Detail & Related papers  (2020-11-24T15:40:27Z)
• An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process [41.049292105761246]
  We critically analyze the review process through a comprehensive study of papers submitted to ICLR between 2017 and 2020. Our findings suggest strong institutional bias in accept/reject decisions, even after controlling for paper quality. We find evidence for a gender gap, with female authors receiving lower scores, lower acceptance rates, and fewer citations per paper than their male counterparts.
  arXiv  Detail & Related papers  (2020-10-11T02:06:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.