arXiv Machine Learning
2d ago

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

Large language models are increasingly used as automated reviewers in scientific evaluation, creating a recursive feedback loop where later reviewers learn from earlier model-generated judgments. A study using Llama 3.1 8B fine‑tuned on ICLR reviews shows that incorporating synthetic reviews compresses rating distributions and reduces semantic diversity, a phenomenon termed scientific‑judgment collapse. To counter this, the authors introduce TrustReviewer, an open‑source LLM system that curates training data and applies paired activation steering at test time to preserve judgment diversity and improve recommendation alignment.

By Sy-Tuyen Ho, Minghui Liu, Furong Huang
arXiv Computation and Language
1d ago

To Consolidate or not to Consolidate? Evaluating the Impact of Consolidation in Multi-Reference Training using Peer Reviews

arXiv:2609.22805v1 Announce Type: new Abstract: Natural language generation (NLG) tasks span the spectrum of conditional entropy, ranging from highly constrained machine translation to open-ended dia...

By Maitreya Prafulla Chitale, Ketaki Mangesh Shetye, Yash More, Harshit Gupta, Manav Chaudhary, Manish Shrivastava, Vasudeva Varma
arXiv Machine Learning
Aug 24

Metag: A dataset to build agentic meta-reviewing capabilities

arXiv:2608.20488v1 Announce Type: new Abstract: AI tools increasingly support tasks across the scientific research cycle, from experiment design and manuscript preparation to peer review. At the same...

By Anirudh Sundar, Min Chen, Divya Tadimeti, Gemma Zhang, Alice Li, Nigel Boachie Kumankumah, Pavan Uttej Ravva, Sadid Hasan, Somya Chatterjee, Pruthvi Prakash Navada, Xiao Wang, Yue Kang, Sulaiman Vesal, Larry Heck