#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 gained an excellent place paper award for his or her work Place: The AI Convention Peer Evaluate Disaster Calls for Creator Suggestions and Reviewer Rewards. We hear from Jaeho concerning the issues they have been attempting to handle, and their proposed writer suggestions mechanism and reviewer reward system.

Might you say one thing about the issue that you just tackle in your place paper?

Our place paper addresses the issues plaguing present AI convention peer evaluate programs, whereas additionally elevating questions concerning the future course of peer evaluate.

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

Submissions to a few of the main AI conferences over the previous few years.

This imbalance has extreme penalties. Nearly all of papers are not receiving sufficient 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 document.

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 in the end hinder real information development. Our place paper focuses on this vital 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 modifications 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 opinions they obtain. This technique permits authors to evaluate reviewers’ comprehension of their work, determine potential indicators of LLM-generated content material, and set up fundamental safeguards in opposition to unfair, biased, or superficial opinions. Importantly, this isn’t about penalizing reviewers, however moderately 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 instant and long-term skilled worth for high quality reviewing. For brief-term motivation, writer analysis scores decide eligibility for digital badges (equivalent to “Prime 10% Reviewer” recognition) that may be displayed on tutorial profiles like OpenReview and Google Scholar. For long-term profession influence, we suggest novel metrics like a “reviewer influence rating” – primarily 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.

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

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 opinions, and all opinions 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 rankings.

This strategy gives three most important advantages. First, it’s sensible – we don’t want to vary current timelines or evaluate templates. The second section may be launched instantly after the authors give suggestions. Second, it protects authors from irresponsible opinions since reviewers know their work might be evaluated. Third, since reviewers sometimes evaluate a number of papers, we will monitor their suggestions scores to assist space chairs determine (ir)accountable reviewers.

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

Might you speak concerning 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.

Brief-term: Digital badges displayed on reviewers’ tutorial profiles, awarded based mostly on writer suggestions scores. The objective is making reviewer contributions extra seen. Whereas some conferences record high reviewers on their web sites, these lists are exhausting to search out. Our badges could 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 advise monitoring measures like an h-index for reviewed papers. These metrics could possibly be included in tutorial portfolios, just like how we at present monitor publication influence.

The core concept is creating tangible profession advantages for reviewers whereas establishing peer evaluate as an expert tutorial service that rewards each authors and reviewers.

What do you assume could possibly be a few of the professionals and cons of implementing this method?

The advantages of our system are threefold. First, it’s a very sensible resolution. Our strategy doesn’t change present evaluate schedules or evaluate burdens, making it straightforward to include into current programs. Second, it encourages reviewers to behave extra responsibly, realizing their work might be evaluated. We emphasize that the majority reviewers already act professionally – nonetheless, even a small variety of irresponsible reviewers can significantly harm the peer evaluate system. Third, with enough scale, writer suggestions scores will make conferences extra sustainable. Space chairs can 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 constructive opinions. Measures to counteract these issues are undoubtedly wanted. We’re at present exploring options to handle this challenge.

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

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

About Jaeho

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

Learn the work in full

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