#ICML2025 excellent place paper: Interview with Jaeho Kim on addressing the issues with convention reviewing

At this yr’s Worldwide Convention on Machine Studying (ICML2025), Jaeho Kim, Yunseok Lee and Seulki Lee received an excellent place paper award for his or her work Place: The AI Convention Peer Overview Disaster Calls for Creator Suggestions and Reviewer Rewards. We hear from Jaeho in regards to the issues they have been attempting to handle, and their proposed writer suggestions mechanism and reviewer reward system.

May you say one thing about the issue that you just deal with in your place paper?

Our place paper addresses the issues plaguing present AI convention peer evaluate techniques, whereas additionally elevating questions in regards to the future path of peer evaluate.

The upcoming downside with the present peer evaluate system in AI conferences is the exponential development in paper submissions pushed by growing curiosity in AI. To place this with numbers, NeurIPS obtained over 30,000 submissions this yr, whereas ICLR noticed a 59.8% enhance in submissions in only one yr. This big enhance in submissions has created a elementary mismatch: whereas paper submissions develop exponentially, the pool of certified reviewers has not saved tempo.

Submissions to among the main AI conferences over the previous few years.

This imbalance has extreme penalties. Nearly all of papers are not receiving ample evaluate high quality, undermining peer evaluate’s important perform as a gatekeeper of scientific information. When the evaluate course of fails, inappropriate papers and flawed analysis can slip via, doubtlessly polluting the scientific report.

Contemplating AI’s profound societal influence, this breakdown in high quality management poses dangers that stretch far past academia. Poor analysis that enters the scientific discourse can mislead future work, affect coverage choices, and finally hinder real information development. Our place paper focuses on this important query and proposes strategies on how we will improve the standard of evaluate, thus main to higher dissemination of information.

What do you argue for within the place paper?

Our place paper proposes two main adjustments to deal with the present peer evaluate disaster: an writer suggestions mechanism and a reviewer reward system.

First, the writer suggestions system allows authors to formally consider the standard of critiques they obtain. This technique permits authors to evaluate reviewers’ comprehension of their work, establish potential indicators of LLM-generated content material, and set up fundamental safeguards towards unfair, biased, or superficial critiques. Importantly, this isn’t about penalizing reviewers, however reasonably creating minimal accountability to guard authors from the small minority of reviewers who might not meet skilled requirements.

Second, our reviewer incentive system gives each fast and long-term skilled worth for high quality reviewing. For brief-term motivation, writer analysis scores decide eligibility for digital badges (resembling “High 10% Reviewer” recognition) that may be displayed on educational profiles like OpenReview and Google Scholar. For long-term profession influence, we suggest novel metrics like a “reviewer influence rating” – basically an h-index calculated from the following citations of papers a reviewer has evaluated. This treats reviewers as contributors to the papers they assist enhance and validates their function in advancing scientific information.

May you inform us extra about your proposal for this new two-way peer evaluate technique?

Our proposed two-way peer evaluate system makes one key change to the present course of: we cut up evaluate launch into two phases.

The authors’ proposed modification to the peer-review system.

Presently, authors submit papers, reviewers write full critiques, and all critiques are launched without delay. In our system, authors first obtain solely the impartial sections – the abstract, strengths, and questions on their paper. Authors then present suggestions on whether or not reviewers correctly understood their work. Solely after this suggestions will we launch the second half containing weaknesses and scores.

This strategy affords three predominant advantages. First, it’s sensible – we don’t want to alter present timelines or evaluate templates. The second section might be launched instantly after the authors give suggestions. Second, it protects authors from irresponsible critiques since reviewers know their work shall be evaluated. Third, since reviewers usually evaluate a number of papers, we will monitor their suggestions scores to assist space chairs establish (ir)accountable reviewers.

The important thing perception is that authors know their very own work finest and may shortly spot when a reviewer hasn’t correctly engaged with their paper.

May you speak in regards to the concrete reward system that you just recommend within the paper?

We suggest each short-term and long-term rewards to handle reviewer motivation, which naturally declines over time regardless of beginning enthusiastically.

Quick-term: Digital badges displayed on reviewers’ educational profiles, awarded primarily based on writer suggestions scores. The purpose is making reviewer contributions extra seen. Whereas some conferences checklist prime reviewers on their web sites, these lists are laborious to seek out. Our badges can be prominently displayed on profiles and will even be printed on convention identify tags.
Instance of a badge that might seem on profiles.

Lengthy-term: Numerical metrics to quantify reviewer influence at AI conferences. We propose monitoring measures like an h-index for reviewed papers. These metrics may very well be included in educational portfolios, just like how we at present monitor publication influence.

The core thought is creating tangible profession advantages for reviewers whereas establishing peer evaluate as knowledgeable educational service that rewards each authors and reviewers.

What do you suppose may very well be among the execs and cons of implementing this technique?

The advantages of our system are threefold. First, it’s a very sensible answer. Our strategy doesn’t change present evaluate schedules or evaluate burdens, making it simple to include into present techniques. Second, it encourages reviewers to behave extra responsibly, realizing their work shall be evaluated. We emphasize that almost all reviewers already act professionally – nevertheless, even a small variety of irresponsible reviewers can significantly injury the peer evaluate system. Third, with ample scale, writer suggestions scores will make conferences extra sustainable. Space chairs could have higher details about reviewer high quality, enabling them to make extra knowledgeable choices about paper acceptance.

Nevertheless, there may be sturdy potential for gaming by reviewers. Reviewers may optimize for rewards by giving overly optimistic critiques. Measures to counteract these issues are undoubtedly wanted. We’re at present exploring options to handle this difficulty.

Are there any concluding ideas you’d like so as to add in regards to the potential future
of conferences and peer-review?

One rising pattern we’ve noticed is the growing dialogue of LLMs in peer evaluate. Whereas we consider present LLMs have a number of weaknesses (e.g., immediate injection, shallow critiques), we additionally suppose they are going to finally surpass people. When that occurs, we’ll face a elementary dilemma: if LLMs present higher critiques, why ought to people be reviewing? Simply because the fast rise of LLMs caught us unprepared and created chaos, we can’t afford a repeat. We must always begin getting ready for this query as quickly as doable.

About Jaeho

Jaeho Kim is a Postdoctoral Researcher at Korea College with Professor Changhee Lee. He obtained his Ph.D. from UNIST below the supervision of Professor Seulki Lee. His predominant analysis focuses on time sequence studying, notably growing basis fashions that generate artificial and human-guided time sequence information to cut back computational and information prices. He additionally contributes to enhancing the peer evaluate course of at main AI conferences, along with his work acknowledged by the ICML 2025 Excellent Place Paper Award.

Learn the work in full

Place: The AI Convention Peer Overview Disaster Calls for Creator Suggestions and Reviewer Rewards, Jaeho Kim, Yunseok Lee, Seulki Lee.