InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification

Yujia Hu, Zhiqiang Hu, Chun Wei Seah, Roy Ka-Wei Lee

Research Area: LMs on diverse modalities and novel applications

Keywords: Authorship Verification, Parameter-Efficient Fine-Tuning (PEFT), InstructAV

TL;DR: We propose the InstructAV framework for Authorship Verification (AV) tasks to accurately determine whether two texts share the same author and to furnish robust linguistic explanations for the AV outcomes.

Abstract: 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.

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Submission Number: 286