mlsanityData to 14 Oct 2026

Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

arXiv 2603.28054 ↗First posted 30 Mar 2026cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 64 references that are in the map. First posted on arXiv 30 Mar 2026.

Builds on

32 of 64 references.

References · 32

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20 more
  1. 2024RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectorscited by 163
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  20. 2025PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learningcited by 3

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Citing papers per year: 2026: 2.

2 papers.