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New TRACE method detects LLM ghostwriters in long texts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called TRACE to detect ghostwriters generated by large language models in long-form texts. This technique creates a unique fingerprint by analyzing token-level transition patterns, such as word rank, using a separate lightweight language model. TRACE has demonstrated state-of-the-art performance on a new dataset called GhostWriteBench, which includes texts over 50,000 words generated by frontier LLMs, and shows robustness in out-of-distribution scenarios and with limited training data. AI

IMPACT Provides a new tool for identifying AI-generated content in long-form writing, impacting content authenticity and copyright.

RANK_REASON The cluster contains an academic paper detailing a new method and dataset for LLM authorship attribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TRACE method detects LLM ghostwriters in long texts

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The cluster contains an academic paper detailing a new method and dataset for LLM authorship attribution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko, Jey Han Lau ·

    Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

    arXiv:2603.28054v2 Announce Type: replace Abstract: In this paper, we introduce GhostWriteBench, a dataset for LLM authorship attribution. It comprises long-form texts (50K+ words per book) generated by frontier LLMs, and is designed to test generalisation across multiple out-of-…

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