AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
unclear 1representative citing papers
Humans detect AI-generated text at 87.6% accuracy across 9 languages and 9 domains, outperforming prior near-random results, and do not always prefer human-written text when the source is unclear.
PeeriScope is an open modular framework that integrates structured features, LLM rubric assessments, and supervised prediction to evaluate peer review quality for self-assessment, editorial triage, and large-scale auditing.
citing papers explorer
-
When AI reviews science: Can we trust the referee?
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
-
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
Humans detect AI-generated text at 87.6% accuracy across 9 languages and 9 domains, outperforming prior near-random results, and do not always prefer human-written text when the source is unclear.
-
PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality
PeeriScope is an open modular framework that integrates structured features, LLM rubric assessments, and supervised prediction to evaluate peer review quality for self-assessment, editorial triage, and large-scale auditing.