Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectors
It addresses the issue of harmful content spread via LLMs for online information credibility, but appears incremental as it builds on existing fine-tuning methods.
The paper tackles the problem of detecting machine-generated text by proposing a robust fine-tuning process for LLMs, resulting in detectors that are more robust against obfuscation and better generalize to out-of-distribution data.
Since the proliferation of LLMs, there have been concerns about their misuse for harmful content creation and spreading. Recent studies justify such fears, providing evidence of LLM vulnerabilities and high potential of their misuse. Humans are no longer able to distinguish between high-quality machine-generated and authentic human-written texts. Therefore, it is crucial to develop automated means to accurately detect machine-generated content. It would enable to identify such content in online information space, thus providing an additional information about its credibility. This work addresses the problem by proposing a robust fine-tuning process of LLMs for the detection task, making the detectors more robust against obfuscation and more generalizable to out-of-distribution data.