The Science of Detecting LLM-Generated Texts
It tackles the problem of potential misuse of LLM-generated texts, such as spreading misinformation and disrupting education, for researchers and policymakers, but is incremental as it is a survey rather than a novel method.
This survey addresses the challenge of detecting texts generated by large language models (LLMs), which are highly sophisticated and nearly indistinguishable from human-written texts, by providing an overview of existing detection techniques and emphasizing future research directions like comprehensive evaluation metrics and threats from open-source LLMs.
The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans. However, this has also sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disruptions in the education system. Although many detection approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This survey aims to provide an overview of existing LLM-generated text detection techniques and enhance the control and regulation of language generation models. Furthermore, we emphasize crucial considerations for future research, including the development of comprehensive evaluation metrics and the threat posed by open-source LLMs, to drive progress in the area of LLM-generated text detection.