CurieLM: Enhancing Large Language Models for Nuclear Domain Applications

Abstract

Large Language Models (LLMs), such as the Mistral model, have exhibited remarkable performance across diverse tasks. However, their efficacy in nuclear applications remains constrained by a lack of domain-specific knowledge and an inability to effectively leverage that knowledge. Nuclear-related tasks, including safety assessments and requirement analyses, pose unique challenges due to the intricate domain expertise and diverse constraints involved. To address these limitations, we introduce CurieLM, an LLM specifically tailored for the nuclear domain. CurieLM builds upon the Mistral model, enhancing its capabilities through domain-specific fine-tuning. Our team of nuclear engineers overcame the initial hurdle of accessing high-quality nuclear data, enabling CurieLM to comprehend and accurately respond to nuclear-specific instructions. This manuscript outlines the development and optimization process of CurieLM, marking a significant step toward enhancing nuclear-related natural language processing tasks. Experimental results demonstrate a 13% performance improvement over base LLMs, underscoring the effectiveness of our approach. Domain-specific LLMs like CurieLM hold a great potential across various applications, and this study sets the stage for further exploration in this emerging field.