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 Overview Disaster Calls for Creator Suggestions and Reviewer Rewards. We hear from Jaeho concerning the issues they have been attempting to deal with, and their proposed creator suggestions mechanism and reviewer reward system.
May you say one thing about the issue that you simply deal with in your place paper?
Our place paper addresses the issues plaguing present AI convention peer assessment methods, whereas additionally elevating questions concerning the future course of peer assessment.
The upcoming drawback with the present peer assessment system in AI conferences is the exponential development 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 big enhance in submissions has created a basic mismatch: whereas paper submissions develop exponentially, the pool of certified reviewers has not stored 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 now not receiving sufficient assessment high quality, undermining peer assessment’s important perform as a gatekeeper of scientific information. When the assessment course of fails, inappropriate papers and flawed analysis can slip by way of, doubtlessly polluting the scientific document.
Contemplating AI’s profound societal influence, this breakdown in high quality management poses dangers that reach 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 crucial query and proposes strategies on how we will improve the standard of assessment, thus main to raised dissemination of data.
What do you argue for within the place paper?
Our place paper proposes two main modifications to deal with the present peer assessment disaster: an creator suggestions mechanism and a reviewer reward system.
First, the creator 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 primary safeguards in opposition to unfair, biased, or superficial critiques. Importantly, this isn’t about penalizing reviewers, however relatively creating minimal accountability to guard authors from the small minority of reviewers who could not meet skilled requirements.
Second, our reviewer incentive system supplies each quick and long-term skilled worth for high quality reviewing. For brief-term motivation, creator analysis scores decide eligibility for digital badges (resembling “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 position in advancing scientific information.
May you inform us extra about your proposal for this new two-way peer assessment technique?
Our proposed two-way peer assessment system makes one key change to the present course of: we break up assessment launch into two phases.
The authors’ proposed modification to the peer-review system.
At present, authors submit papers, reviewers write full critiques, and all critiques are launched directly. 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 most important advantages. First, it’s sensible – we don’t want to vary current timelines or assessment templates. The second part may be launched instantly after the authors give suggestions. Second, it protects authors from irresponsible critiques since reviewers know their work will probably be evaluated. Third, since reviewers usually assessment 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 greatest and might shortly spot when a reviewer hasn’t correctly engaged with their paper.
May you discuss concerning the concrete reward system that you simply counsel within the paper?
We suggest each short-term and long-term rewards to deal with reviewer motivation, which naturally declines over time regardless of beginning enthusiastically.
Quick-term: Digital badges displayed on reviewers’ tutorial profiles, awarded primarily based on creator suggestions scores. The aim is making reviewer contributions extra seen. Whereas some conferences listing prime reviewers on their web sites, these lists are exhausting to seek 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 recommend monitoring measures like an h-index for reviewed papers. These metrics could possibly be included in tutorial portfolios, much like how we presently monitor publication influence.
The core concept is creating tangible profession advantages for reviewers whereas establishing peer assessment as knowledgeable tutorial service that rewards each authors and reviewers.
What do you suppose 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 assessment schedules or assessment burdens, making it simple to include into current methods. Second, it encourages reviewers to behave extra responsibly, figuring out their work will probably be evaluated. We emphasize that the majority reviewers already act professionally – nevertheless, even a small variety of irresponsible reviewers can significantly harm the peer assessment system. Third, with ample scale, creator suggestions scores will make conferences extra sustainable. Space chairs may have higher details about reviewer high quality, enabling them to make extra knowledgeable choices about paper acceptance.
Nonetheless, there’s 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 presently exploring options to deal with this subject.
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 assessment. Whereas we imagine present LLMs have a number of weaknesses (e.g., immediate injection, shallow critiques), we additionally suppose they may finally surpass people. When that occurs, we’ll face a basic 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 making ready for this query as quickly as attainable.
About Jaeho
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Jaeho Kim is a Postdoctoral Researcher at Korea College with Professor Changhee Lee. He acquired his Ph.D. from UNIST below 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 information to scale back computational and information prices. He additionally contributes to enhancing the peer assessment 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.
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is a non-profit devoted to connecting the AI neighborhood to the general public by offering free, high-quality data in AI.
