aiqbench analysis
This paper presents GhostWriteBench, a new dataset designed to test the robustness of LLM authorship attribution across diverse domains and unseen models. The authors also introduce TRACE, a lightweight fingerprinting method that analyzes token-level transition patterns to identify specific model origins in long-form generated texts like books. These contributions advance the field by addressing critical challenges in detecting synthetic content and understanding the stylistic signatures of frontier language models.
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
arXiv:2603.28054v3 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:2603.28054v3 [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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