MOSAIC: Multiple Observers Spotting AI Content, a Robust Approach to Machine-Generated Text Detection

ICLR 2025 Conference Submission12364 Authors

Keywords: Machine-generated text detection, Mixture of models, Optimal detector, Robustness

Abstract: The dissemination of Large Language Models (LLMs), trained at scale, and endowed with powerful text-generating abilities has vastly increased the threats posed by generative AI technologies by reducing the cost of producing harmful, toxic, faked or forged content. In response, various proposals have been made to automatically discriminate artificially generated from human-written texts, typically framing the problem as a classification problem. Most approaches evaluate an input document by a well-chosen detector LLM, assuming that low-perplexity scores reliably signal machine-made content. As using one single detector can induce brittleness of performance, we instead consider several and derive a new, theoretically grounded approach to combine their respective strengths. Our experiments, using a variety of generator LLMs, suggest that our method effectively increases the robustness of detection.

Primary Area: generative models

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Submission Number: 12364