Detecting Machine-Generated Long-Form Content with Latent-Space Variables
Abstract: The increasing capability of large language models (LLMs) to generate fluent long-form texts is presenting new challenges in distinguishing these outputs from those of humans. Existing zero-shot detectors that primarily focus on token-level distributions are vulnerable to real-world domain shift including different decoding strategies, variations in prompts, and attacks. We propose a more robust method that incorporates abstract elements---such as topic or event transitions---as key deciding factors, by training a latent-space model on sequences of events or topics derived from human-written texts.
On three different domains, machine generations which are originally inseparable from humans' 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 such as GPT-4 selecting event triggers and transitions differently, and inherent disparity regardless of the generation configurations adopted in real-time.
Paper Type: Long
Research Area: Language Modeling
Research Area Keywords: machine content detection, robustness
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 2307
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