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

TL;DR

This paper presents InstructAV, a novel method that fine-tunes large language models with parameter-efficient techniques to improve authorship verification accuracy and explainability, achieving state-of-the-art results.

Contribution

InstructAV introduces a new fine-tuning approach that enhances both accuracy and interpretability in authorship verification tasks.

Findings

Achieves state-of-the-art accuracy on multiple datasets.

Provides transparent and understandable explanations for decisions.

Demonstrates improved performance over existing methods.

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.