Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification
This addresses authorship verification for applications like forensic analysis and plagiarism detection, offering an incremental improvement with better interpretability.
The paper tackled authorship verification by proposing PromptAV, a method using large-language models with step-by-step prompts, which outperformed state-of-the-art baselines, operated effectively with limited data, and enhanced interpretability.
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.