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The article critiques the concept of AI-generated text and the effectiveness of watermarking for proving AI involvement, arguing that it may not capture human contribution and highlights ambiguity in defining AI slop.
A study measures the prevalence of AI-written text on arXiv, finding that over 30% of new submissions read as machine-written, with computer science leading at 65% and mathematics lowest at 0.7%.
A large-scale empirical study analyzes 284 linguistic features across 27 LLMs and 10 text domains to assess which features reliably detect AI-generated text. The study finds that lexical richness measures are the most robust cross-domain and cross-model signals, while many other proposed indicators are strongly context-dependent.
This paper presents findings from the Counter Turing Test shared task on AI-generated text detection, with top systems achieving perfect binary classification but significantly lower performance in model attribution, highlighting the difficulty of distinguishing outputs from different large language models.
A winner of the 2026 Commonwealth Short Story Prize has been accused of using AI to generate the story, sparking debate about AI's role in literature and authenticity.
DetectRL-X is a comprehensive multilingual benchmark for evaluating LLM-generated text detectors across 8 languages and 6 domains, including stress testing with AI-assisted writing operations and perturbations. It reveals strengths and limitations of current detectors in multilingual scenarios.