Large language models remain readily distinguishable from human text even after extensive calibration, with automated classifiers detecting AI-generated social media posts with 70 to 80 per cent accuracy, according to research introducing a computational Turing test that reveals systematic differences between human and AI language.
Researchers at University of Zurich, University of Amsterdam, Duke University and New York University systematically compared nine open-weight LLMs across five calibration strategies, including fine-tuning, stylistic prompting and context retrieval, benchmarking their ability to reproduce user interactions on X (formerly Twitter), Bluesky and Reddit. The findings were published in arXiv.
Researchers at University of Zurich, University of Amsterdam, Duke University and New York University systematically compared nine open-weight LLMs across five calibration strategies, including fine-tuning, stylistic prompting and context retrieval, benchmarking their ability to reproduce user interactions on X (formerly Twitter), Bluesky and Reddit. The findings were published in arXiv.