Who Wrote it and Why? Prompting Large-Language Models for Authorship
  Verification
Chia-Yu Hung Zhiqiang Hu Yujia Hu Roy Ka-Wei Lee
Authorship verification (AV) is a fundamental task in natural language
processing (NLP) and computational linguistics, with applications in forensic
analysis, plagiarism detection, and identification of deceptive content.
Existing AV techniques, including traditional stylometric and deep learning
approaches, face limitations in terms of data requirements and lack of
explainability. To address these limitations, this paper proposes PromptAV, a
novel technique that leverages Large-Language Models (LLMs) for AV by providing
step-by-step stylometric explanation prompts. PromptAV outperforms
state-of-the-art baselines, operates effectively with limited training data,
and enhances interpretability through intuitive explanations, showcasing its
potential as an effective and interpretable solution for the AV task.