Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness
<|reference_start|>Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness: The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLM-generated text. Zero-shot detectors, due to their training-free nature, have received considerable attention and notable success. In this paper, we identify a new feature, token cohesiveness, that is useful for zero-shot detection, and we demonstrate that LLM-generated text tends to exhibit higher token cohesiveness than human-written text. Based on this observation, we devise TOCSIN, a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors. To calculate token cohesiveness, TOCSIN only requires a few rounds of random token deletion and semantic difference measurement, making it particularly suitable for a practical black-box setting where the source model used for generation is not accessible. Extensive experiments with four state-of-the-art base detectors on various datasets, source models, and evaluation settings demonstrate the effectiveness and generality of the proposed approach. Code available at: \url{https://github.com/Shixuan-Ma/TOCSIN}.<|reference_end|>
@article{ma2024zero-shot,
    title={Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness},
    author={Shixuan Ma and Quan Wang},
    journal={arXiv preprint arXiv:2409.16914},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16914},
    primaryClass={cs.CL}
}