ExaGPT uses span-level similarity retrieval from human and LLM datastores to detect machine-generated text while supplying the matching spans as human-interpretable evidence, achieving up to 37-point accuracy gains over prior interpretable detectors at 1% FPR.
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BIG-bench is a 204-task benchmark that measures scaling trends, calibration, and absolute limitations of language models across knowledge, reasoning, and social domains.
Sem-Detect detects AI-generated peer reviews via semantic claim comparison to multiple AI-generated versions of the same paper, achieving a 25.5% improvement in TPR at 0.1% FPR over baselines on over 20,000 ICLR and NeurIPS reviews.
People show little ability to distinguish AI-generated from human-written health advice in online communities, with detection varying by health condition and unreliable heuristics.
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.
citing papers explorer
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ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability
ExaGPT uses span-level similarity retrieval from human and LLM datastores to detect machine-generated text while supplying the matching spans as human-interpretable evidence, achieving up to 37-point accuracy gains over prior interpretable detectors at 1% FPR.
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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench is a 204-task benchmark that measures scaling trends, calibration, and absolute limitations of language models across knowledge, reasoning, and social domains.
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Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
Sem-Detect detects AI-generated peer reviews via semantic claim comparison to multiple AI-generated versions of the same paper, achieving a 25.5% improvement in TPR at 0.1% FPR over baselines on over 20,000 ICLR and NeurIPS reviews.
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Discerning Authorship in Online Health Communities: Experience, Trust, and Transparency Implications for Moderating AI
People show little ability to distinguish AI-generated from human-written health advice in online communities, with detection varying by health condition and unreliable heuristics.
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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.