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    <abstract>Out-of-distribution (OOD) generalisation is essential for authorship attribution (AA), especially when involving new authors, yet this aspect remains insufficiently supported by existing AA datasets. In this paper, we introduce GHOSTWRITEBENCH, a dataset for LLM AA task, designed to test generalisation across multiple OOD dimensions, including unseen domain and authors. GHOSTWRITEBENCH comprises long-form texts (50K+ words per book) generated by ten frontier LLMs. We find that existing methods degrade substantially in unseen author scenarios — dropping by as much as 70% even under high-resource settings. We propose TRACE, a novel lightweight and interpretable fingerprinting method that captures token-level transition patterns (i.e., word rank and entropy) using a lightweight language model. Experiments show robustness of TRACE in OOD settings and establish it as a strong baseline on GHOSTWRITEBENCH.</abstract>
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%0 Conference Proceedings
%T Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
%A Shetty, Anudeex
%A Xu, Qiongkai
%A Ohrimenko, Olga
%A Lau, Jey Han
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F shetty-etal-2026-wrote
%X Out-of-distribution (OOD) generalisation is essential for authorship attribution (AA), especially when involving new authors, yet this aspect remains insufficiently supported by existing AA datasets. In this paper, we introduce GHOSTWRITEBENCH, a dataset for LLM AA task, designed to test generalisation across multiple OOD dimensions, including unseen domain and authors. GHOSTWRITEBENCH comprises long-form texts (50K+ words per book) generated by ten frontier LLMs. We find that existing methods degrade substantially in unseen author scenarios — dropping by as much as 70% even under high-resource settings. We propose TRACE, a novel lightweight and interpretable fingerprinting method that captures token-level transition patterns (i.e., word rank and entropy) using a lightweight language model. Experiments show robustness of TRACE in OOD settings and establish it as a strong baseline on GHOSTWRITEBENCH.
%U https://aclanthology.org/2026.emnlp-main.2525/
%P 57419-57447

Markdown (Informal)

[Who Wrote the Book? Detecting and Attributing LLM Ghostwriters](https://aclanthology.org/2026.emnlp-main.2525/) (Shetty et al., EMNLP 2026)

• Who Wrote the Book? Detecting and Attributing LLM Ghostwriters (Shetty et al., EMNLP 2026)

ACL

• Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko, and Jey Han Lau. 2026. Who Wrote the Book? Detecting and Attributing LLM Ghostwriters. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57419–57447, Budapest, Hungary. Association for Computational Linguistics.