Automated Peer Review
Overview
Automated peer review studies how computational systems can evaluate manuscripts, identify weaknesses, check claims, and assist tasks such as reviewer matching, rebuttal, and meta-review. Common approaches include prompting and fine-tuning language models, retrieval-augmented and multi-agent review, rubric-based scoring, and tool-assisted checks of references, code, or experiments. Evaluation uses conference-paper datasets, expert annotations, outcome prediction, and injected-error or claim-verification benchmarks to test dimensions such as correctness, coverage, calibration, and actionability. Systems can provide useful and sometimes complementary feedback, but unreliable judgments, hallucinations, bias, and vulnerability to strategic rewriting or prompt injection leave human oversight and robust evaluation central concerns.
Latest papers
109 papers11 papers in the last four weeks, down 21% on the four weeks before. 0.1% of all new papers.