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AI Research
arXiv CS.AI·Jun 12·10 min read
Authorship Attribution in Multilingual Machine-Generated Texts
TL;DR
As Large Language Models (LLMs) become more sophisticated, distinguishing between machine-generated text and human-written content is increasingly challenging. This study introduces a method for Multilingual Authorship Attribution (AA), which identifies the source of text across 18 languages and 8 generators, including both LLMs and human authors.
✦ Why It Matters
Engineers and researchers can enhance authorship detection systems by considering multilingual capabilities and adapting existing methods for broader applications.
Key Takeaways
- Explore multilingual capabilities in authorship detection systems.
- Adapt existing monolingual methods for multilingual contexts.
- Address limitations in cross-lingual transferability of AA techniques.
- Develop more robust approaches for real-world authorship attribution.
Full Summary
The rise of Large Language Models (LLMs) has made it difficult to differentiate between machine-generated text (MGT) and human-written content, especially as these models achieve human-like fluency. This research addresses the gap in authorship attribution (AA) by proposing a Multilingual Authorship Attribution framework that evaluates texts in 18 languages from 8 different generators, including 7 LLMs and human authors.
The methodology involved testing existing monolingual AA techniques for their effectiveness in multilingual contexts, focusing on cross-lingual transferability. Findings revealed that while some monolingual methods can be adapted, they face significant limitations when applied to diverse language families.
The study highlights the complexity of multilingual AA and emphasizes the need for more robust approaches to accurately attribute authorship in real-world scenarios. These insights are crucial for developing better detection tools for distinguishing between human and machine-generated content.