Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

arXiv cs.CL / 3/31/2026

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Key Points

  • The paper introduces GhostWriteBench, a new dataset for LLM authorship attribution using long-form (50K+ words) book-length texts generated by frontier models to evaluate generalization across multiple OOD dimensions.
  • It also proposes TRACE, an interpretable and lightweight fingerprinting approach that infers token-level transition patterns (such as word-rank changes) using a separate lightweight language model.
  • TRACE is designed to work with both open- and closed-source LLMs, addressing a key challenge in real-world attribution where model access may be restricted.
  • Experiments reported on GhostWriteBench indicate TRACE delivers state-of-the-art results, maintains robustness under OOD conditions, and performs effectively with limited training data.

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.