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GhostWriteBench introduces a robust dataset for testing LLM authorship attribution across diverse domains and unseen models. The proposed TRACE method offers a lightweight, interpretable fingerprinting approach that captures token-level transition patterns to identify generative sources effectively. This advancement provides a practical tool for verifying content provenance in both open- and closed-source model environments.
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Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

arXiv2 min read

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-distribution (OOD) dimensions, including domain and unseen LLM author. We also propose TRACE -- a novel fingerprinting method that is interpretable and lightweight -- that works for both open- and closed-source models. TRACE creates the fingerprint by capturing token-level transition patterns (e.g., word rank) estimated by another lightweight language model. Experiments on GhostWriteBench demonstrate that TRACE achieves state-of-the-art performance, remains robust in OOD settings, and works well in limited training data scenarios.

Title:Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

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-distribution (OOD) dimensions, including domain and unseen LLM author. We also propose TRACE -- a novel fingerprinting method that is interpretable and lightweight -- that works for both open- and closed-source models. TRACE creates the fingerprint by capturing token-level transition patterns (e.g., word rank) estimated by another lightweight language model. Experiments on GhostWriteBench demonstrate that TRACE achieves state-of-the-art performance, remains robust in OOD settings, and works well in limited training data scenarios.

Computation and Language (cs.CL)

arXiv:2603.28054 [cs.CL]

(or arXiv:2603.28054v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.28054 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 206 words

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