AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning

TL;DR

AnyTaskTune introduces a task-specific fine-tuning approach for LLMs, significantly improving performance on diverse domain-specific tasks across multiple sectors by creating specialized datasets and benchmarks.

Contribution

The paper presents a novel Task-Fine-Tune methodology that enhances domain-specific model performance through targeted sub-task identification and dataset creation, with comprehensive experiments across various sectors.

Findings

Models fine-tuned with Task-Fine-Tune outperform general models on domain tasks.

The approach improves performance on legal, finance, healthcare, and other sector-specific tasks.

Open-source datasets facilitate community engagement and further research.

Abstract

The pervasive deployment of Large Language Models-LLMs in various sectors often neglects the nuanced requirements of individuals and small organizations, who benefit more from models precisely tailored to their specific business contexts rather than those with broadly superior general capabilities. This work introduces \textbf{AnyTaskTune}, a novel fine-tuning methodology coined as \textbf{Task-Fine-Tune}, specifically developed to elevate model performance on a diverse array of domain-specific tasks. This method involves a meticulous process to identify and define targeted sub-tasks within a domain, followed by the creation of specialized enhancement datasets for fine-tuning, thereby optimizing task-specific model performance. We conducted comprehensive fine-tuning experiments not only in the legal domain for tasks such as keyword extraction and sentence prediction but across over twenty different sub-tasks derived from the domains of finance, healthcare, law, psychology, consumer services, and human resources. To substantiate our approach and facilitate community engagement, we will open-source these bilingual task datasets. Our findings demonstrate that models fine-tuned using the \textbf{Task-Fine-Tune} methodology not only achieve superior performance on these specific tasks but also significantly outperform models with higher general capabilities in their respective domains. Our work is publicly available at \url{https://github.com/PandaVT/DataTager}.