AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
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....
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Zusammenfassung: | 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}. |
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DOI: | 10.48550/arxiv.2407.07094 |