HalluPeer is a new benchmark designed to detect hallucinations in scientific peer reviews. It provides aligned triples of paper content, human-written reviews, and hallucination-injected reviews, annotated for detection, classification, and localization. Experiments on 12K papers and 38K reviews show that existing detectors struggle to separate hallucinations from legitimate critique, and that HalluPeer-defined hallucination patterns occur in real peer reviews.
By Tzu-Ling Lin, Dong-Ting Yao, Teng-Fang Hsiao, Wei-Chih Chen, Hong-Han Shuai
arXiv:2604.01461v2 Announce Type: replace
Abstract: Reducing hallucinations in Large Language Models (LLMs) is essential for accurate data extraction from large text corpora. Current methods, like pr...
By Daniel Xie, Maxwell J. Jacobson, Adil Wazeer, Haiyan Wang, Xinghang Zhang, Yexiang Xue
arXiv:2603. 20450v2 Announce Type: replace-cross Abstract: A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews.
By Rounak Saha, Gurusha Juneja, Dayita Chaudhuri, Naveeja Sajeevan, Nihar B Shah, Danish Pruthi
arXiv:2606.21359v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used to communicate and explain scientific concepts, yet their tendency to hallucinate poses signific...
By Raia Abu Ahmad, Nikolas Rauscher, Ekaterina Borisova, Fabio Barth, Georg Rehm, Sebastian M\"oller
arXiv:2609.25046v1 Announce Type: new
Abstract: Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely...
By Alireza Daghighfarsoodeh, Sajad Ebrahimi, Ali Ghorbanpour, Soroush Sadeghian, Radin Cheraghi, Negar Arabzadeh, Ebrahim Bagheri
Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence.