CMU Researchers Explore ‘Crazy Idea’ of Automating AI Paper Reviews
The number of AI-related research papers has skyrocketed in recent years, and the burden this has placed on reviewers at major academic conferences has been well-documented. The trend shows no sign of slowing, and this led a bold Carnegie Mellon University (CMU) team to explore the prospect of using AI to review AI papers.
Yes, the researchers acknowledge that automating paper reviews is a “crazy idea,” but the Review Advisor project introduced in their paper Can We Automate Scientific Reviewing? has revealed some interesting things. Here’s a very meta example — a review of the new CMU paper that was generated by the paper-review model itself:
“This paper proposes to use NLP models to generate reviews for scientific papers. The model is trained on the ASAP-Review dataset and evaluated on a set of metrics to evaluate the quality of the generated reviews. It is found that the model is not very good at summarizing the paper, but it is able to generate more detailed reviews that cover more aspects of the paper than those created by humans. The paper also finds that both human and automatic reviewers exhibit varying degrees of bias and biases, and that the system generate more biased reviews than human reviewers.”
As seen above and in experiment results, the proposed ReviewAdvisor can often capture and explain a paper’s core idea with some precision. Overall, however, the researchers concede that the system also tends to generate non-factual statements in its paper assessments, “which is a serious flaw in a high-stakes setting such as reviewing.” They suggest that such systems could still be useful by providing a starting point for human reviewers and potentially guiding junior reviewers.
The team approached the challenge of automating paper reviews by first defining what a good review is. They referenced guidelines from top academic conferences such as ICML, NeurIPS, ICLR and other resources, summarizing the most frequently mentioned qualities of a good review as follows: