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

TL;DR

The paper tackles the mismatch between broad LLM capabilities and the precise needs of domain-specific tasks faced by individuals and small organizations. It introduces AnyTaskTune, a Task-Fine-Tune framework that uses Explicit Data Sets and the DataTager data-synthesis pipeline to produce domain-tuned models. Across finance, healthcare, law, psychology, and role-play, the approach yields substantial gains on targeted tasks and often surpasses higher-capability general models, though cross-domain generalization remains limited. By releasing over twenty bilingual task datasets and outlining a practical deployment workflow, the work provides a usable pathway for industry-focused AI adoption, with DataTager serving as the data-generation backbone (not open-sourced).

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}.

Figures (3)

• Figure 1: AnyTaskTune Framework
• Figure 2: Explicit and Implicit DataSets
• Figure 3: Task examples