Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI

Understand

Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random guessing.

• To verify the generalizability of this finding across languages and domains, we perform an extensive case study to identify the upper bound of human detection accuracy.
• Across 16 datasets covering 9 languages and 9 domains, 19 annotators achieved an average detection accuracy of 87.6%, thus challenging previous conclusions.
• We find that major gaps between human and machine text lie in concreteness, cultural nuances, and diversity.