  Large Language Models (LLMs) have demonstrated remarkable proficiency in a
wide range of NLP tasks. However, when it comes to authorship verification (AV)
tasks, which involve determining whether two given texts share the same
authorship, even advanced models like ChatGPT exhibit notable limitations. This
paper introduces a novel approach, termed InstructAV, for authorship
verification. This approach utilizes LLMs in conjunction with a
parameter-efficient fine-tuning (PEFT) method to simultaneously improve
accuracy and explainability. The distinctiveness of InstructAV lies in its
ability to align classification decisions with transparent and understandable
explanations, representing a significant progression in the field of authorship
verification. Through comprehensive experiments conducted across various
datasets, InstructAV demonstrates its state-of-the-art performance on the AV
task, offering high classification accuracy coupled with enhanced explanation
reliability.
