Detecting Machine-Generated Long-Form Content with Latent-Space Variables
<|reference_start|>Detecting Machine-Generated Long-Form Content with Latent-Space Variables: The increasing capability of large language models (LLMs) to generate fluent long-form texts is presenting new challenges in distinguishing machine-generated outputs from human-written ones, which is crucial for ensuring authenticity and trustworthiness of expressions. Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts, including different prompting and decoding strategies, and adversarial attacks. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts by training a latent-space model on sequences of events or topics derived from human-written texts. In three different domains, machine-generated texts, which are originally inseparable from human texts on the token level, can be better distinguished with our latent-space model, leading to a 31% improvement over strong baselines such as DetectGPT. Our analysis further reveals that, unlike humans, modern LLMs like GPT-4 generate event triggers and their transitions differently, an inherent disparity that helps our method to robustly detect machine-generated texts.<|reference_end|>
@article{tian2024detecting,
    title={Detecting Machine-Generated Long-Form Content with Latent-Space
  Variables},
    author={Yufei Tian, Zeyu Pan, Nanyun Peng},
    journal={arXiv preprint arXiv:2410.03856},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03856},
    primaryClass={cs.CL cs.LG}
}