提出新方法检测大模型生成文本的作者，可识别不同模型且数据少也能用。
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
- 用轻量语言模型捕捉词元转移模式生成指纹。
- 在跨领域和未知模型上仍保持高准确率，小样本下表现稳定。
- 适合需文本溯源或模型审计的研究与安全应用。
本文提出GhostWriteBench，一个用于大模型作者归属的基准数据集，包含由前沿大模型生成的长篇文本（每本书超过5万字），旨在测试模型在多个分布外（OOD）维度下的泛化能力，包括领域差异和未见过的大模型作者。同时提出TRACE——一种可解释、轻量级的指纹方法，适用于开源与闭源模型。TRACE通过另一轻量语言模型估计词元级转移模式（如词频排名）生成指纹。在GhostWriteBench上的实验表明，TRACE达到当前最佳性能，在分布外场景下依然稳健，且在训练数据有限时表现良好。
原文摘要 · Abstract (English)
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.
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