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        <title>Detecting Machine-Generated Long-Form Content with Latent-Space Variables</title>
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            <namePart type="given">Mohit</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Detecting Machine-Generated Long-Form Content with Latent-Space Variables
%A Tian, Yufei
%A Pan, Zeyu
%A Peng, Nanyun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F tian-etal-2024-detecting
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.608
%P 10394-10408

Markdown (Informal)

[Detecting Machine-Generated Long-Form Content with Latent-Space Variables](https://aclanthology.org/2024.findings-emnlp.608) (Tian et al., Findings 2024)

• Detecting Machine-Generated Long-Form Content with Latent-Space Variables (Tian et al., Findings 2024)

ACL

• Yufei Tian, Zeyu Pan, and Nanyun Peng. 2024. Detecting Machine-Generated Long-Form Content with Latent-Space Variables. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10394–10408, Miami, Florida, USA. Association for Computational Linguistics.