Résultats du processus de synthèse de l'article arXiv : 2304.04736v1

Résumé : Our work focuses on the challenge of detecting outputs generated by Large Language Models (LLMs) from those generated by humans. The ability to distinguish between the two is of utmost importance in numerous applications. However, the possibility and impossibility of such discernment have been subjects of debate within the community. Therefore, a central question is whether we can detect AI-generated text and, if so, when. In this work, we provide evidence that it should almost always be possible to detect the AI-generated text unless the distributions of human and machine generated texts are exactly the same over the entire support. This observation follows from the standard results in information theory and relies on the fact that if the machine text is becoming more like a human, we need more samples to detect it. We derive a precise sample complexity bound of AI-generated text detection, which tells how many samples are needed to detect. This gives rise to additional challenges of designing more complicated detectors that take in n samples to detect than just one, which is the scope of future research on this topic. Our empirical evaluations support our claim about the existence of better detectors demonstrating that AI-Generated text detection should be achievable in the majority of scenarios. Our results emphasize the importance of continued research in this area