Scientific Domain

Research on Large Language Models (LLMs) in scientific domains focuses on adapting and evaluating these models for various tasks, from literature understanding and data analysis to question answering and education. Current efforts concentrate on developing robust benchmarks to assess LLMs' safety, accuracy, and reasoning capabilities across diverse scientific disciplines, employing techniques like continual pre-training, fine-tuning, and multimodal integration with methods such as object detection and natural language processing. This work aims to improve the reliability and trustworthiness of LLMs in scientific research, ultimately accelerating discovery and enhancing the accessibility of scientific information. The development of domain-specific LLMs and improved evaluation metrics are key to realizing this potential.