When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation

Hugging Face Daily Papers Papers
ai-peer-review scientific-judgment-collapse trust-reviewer llm-fine-tuning mitigation feedback-loop academic-evaluation

Summary

This paper studies the feedback loop where AI-generated reviews influence future training of AI reviewers, leading to reduced judgment diversity called 'scientific-judgment collapse,' and introduces TrustReviewer, an open-source system to mitigate this through curated training and activation steering.

Large language models (LLMs) increasingly participate in scientific evaluation, both as automated reviewers and as assistants to human reviewers. As model-generated reviews enter public data and future training corpora, AI peer review can become recursive: later reviewers learn from judgments produced by earlier models. We study one step of this feedback loop in a controlled setting. Starting from Llama 3.1 8B, we first fine-tune a reviewer on official ICLR reviews from 2018--2023 and then train four successor models on ICLR 2024 data with systematically varied mixtures of official and model-generated reviews. Our study shows that introducing synthetic reviews compresses rating distributions and reduces both same-paper and corpus-level semantic diversity. We call this pattern scientific-judgment collapse. To mitigate this failure mode, we introduce TrustReviewer, an open-source LLM-based system for generating peer reviews of AI and machine learning papers. TrustReviewer intervenes at two complementary stages. For training-time prevention, we train the core reviewer in a single stage on a curated corpus designed to reduce low-quality and semantically degenerate supervision. For test-time correction, paired activation steering aims to further mitigate residual tendencies toward collapsed judgments without further training or additional expert annotation. Together, these results characterize a concrete risk of recursive reviewer training and provide practical interventions for preserving judgment diversity and improving recommendation alignment in AI-assisted scientific evaluation.
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Source: https://huggingface.co/papers/2609.20942

Abstract

Largelanguagemodels(LLMs)increasinglyparticipateinscientificevaluation,bothasautomatedreviewersandasassistantstohumanreviewers.Asmodel-generatedreviewsenterpublicdataandfuturetrainingcorpora,AIpeerreviewcanbecomerecursive:laterreviewerslearnfromjudgmentsproducedbyearliermodels.Westudyonestepofthisfeedbackloopinacontrolledsetting.StartingfromLlama3.18B,wefirstfine-tunearevieweronofficialICLRreviewsfrom2018--2023andthentrainfoursuccessormodelsonICLR2024datawithsystematicallyvariedmixturesofofficialandmodel-generatedreviews.Ourstudyshowsthatintroducingsyntheticreviewscompressesratingdistributionsandreducesbothsame-paperandcorpus-levelsemanticdiversity.Wecallthispatternscientific-judgmentcollapse.Tomitigatethisfailuremode,weintroduceTrustReviewer,anopen-sourceLLM-basedsystemforgeneratingpeerreviewsofAIandmachinelearningpapers.TrustReviewerintervenesattwocomplementarystages.Fortraining-timeprevention,wetrainthecorereviewerinasinglestageonacuratedcorpusdesignedtoreducelow-qualityandsemanticallydegeneratesupervision.Fortest-timecorrection,pairedactivationsteeringaimstofurthermitigateresidualtendenciestowardcollapsedjudgmentswithoutfurthertrainingoradditionalexpertannotation.Together,theseresultscharacterizeaconcreteriskofrecursivereviewertrainingandprovidepracticalinterventionsforpreservingjudgmentdiversityandimprovingrecommendationalignmentinAI-assistedscientificevaluation.

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