The ability to accurately identify authorship is crucial for verifying
content authenticity and mitigating misinformation. Large Language Models
(LLMs) have demonstrated exceptional capacity for reasoning and
problem-solving. However, their potential in authorship analysis, encompassing
authorship verification and attribution, remains underexplored. This paper
conducts a comprehensive evaluation of LLMs in these critical tasks.
Traditional studies have depended on hand-crafted stylistic features, whereas
state-of-the-art approaches leverage text embeddings from pre-trained language
models. These methods, which typically require fine-tuning on labeled data,
often suffer from performance degradation in cross-domain applications and
provide limited explainability. This work seeks to address three research
questions: (1) Can LLMs perform zero-shot, end-to-end authorship verification
effectively? (2) Are LLMs capable of accurately attributing authorship among
multiple candidates authors (e.g., 10 and 20)? (3) How can LLMs provide
explainability in authorship analysis, particularly through the role of
linguistic features? Moreover, we investigate the integration of explicit
linguistic features to guide LLMs in their reasoning processes. Our extensive
assessment demonstrates LLMs' proficiency in both tasks without the need for
domain-specific fine-tuning, providing insights into their decision-making via
a detailed analysis of linguistic features. This establishes a new benchmark
for future research on LLM-based authorship analysis. The code and data are
available at this https URL

使用大型语言模型进行作者身份验证和归属度分析的研究，以及通过详细分析语言特征为决策提供解释，为未来基于大型语言模型的作者身份分析研究建立了新的基准。