arXiv:2508.01656v2 Announce Type: replace Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult. While early efforts in MGT detection have focused on binary classification, the growing landscape and diversity of LLMs require a more fine-grained yet challenging authorship attribution (AA), i.e., being able to identify the precise generator (LLM or human) behind a text. However, AA remains nowadays confined to a monolingual setting, with English being the most
The rapid advancement of Large Language Models (LLMs) to human-like fluency makes distinguishing machine-generated content increasingly challenging and critical.
Accurate authorship attribution is crucial for maintaining trust in information, intellectual property, and combating disinformation in an AI-pervasive world.
The focus is shifting from basic binary detection of machine-generated text to more sophisticated identification of specific LLM or human authors, even across languages.
- · Digital forensics companies
- · AI ethicists
- · Content verification platforms
- · Malicious actors deploying LLMs
- · Undifferentiated content farms
- · Monolingual AI detection tools
Improved methods for identifying the source of text content, whether human or specific AI models.
Increased accountability for content creators and a potential reduction in disinformation generated by AI.
The development of 'AI watermarking' or provable authorship technologies to pre-empt attribution challenges.
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Read at arXiv cs.CL