Papers by Matthieu Dubois
MOSAIC: Multiple Observers Spotting AI Content (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made it easier for all to produce harmful, toxic, faked or forged content. |
| Approach: | They propose to use large language models to automatically discriminate from human-written texts by comparing their probability distributions over a document to see if they can detect forged or harmful content. |
| Outcome: | The proposed approach harnesses each model’s capabilities, leading to strong detection performance on a variety of domains. |
How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study (2025.findings-emnlp)
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| Challenge: | Recent detectors report near-perfect accuracy, often boasting AUROC scores above 99%, but these claims typically assume fixed generation settings, leaving open the question of how robust such systems are to changes in decoding strategies. |
| Approach: | They examine how sampling-based decoding impacts detectability with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. |
| Outcome: | The proposed framework systematically examines how sampling-based decoding impacts detectability, with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. |