Collaborative Integration of Large Language Models and Computer Algebra Systems for Simulation Verification and Code Generation

This paper explores the transformative role of Large Language Models (LLMs) in scientific computing and research. As scientific datasets increasingly contain unstructured or semi-structured text, preprocessing and structuring this data are crucial challenges. LLMs have shown great potential in automating data extraction, transforming raw text into structured formats for computational models. By utilizing techniques such as Named Entity Recognition (NER), LLMs can extract critical information like chemical names, experimental conditions, and research findings, significantly reducing manual efforts. In addition to data preprocessing, LLMs facilitate literature reviews by rapidly scanning vast amounts of research papers, summarizing key insights, and constructing knowledge graphs to visualize relationships across complex datasets. Furthermore, LLMs contribute to problem-solving by generating theoretical insights and assisting in mathematical and computational tasks. Finally, LLMs can support scientific programming by automating code generation for data analysis and simulations. These capabilities enhance efficiency, foster faster discoveries, and lower the barrier to managing complex scientific data, ultimately accelerating research in multiple scientific domains.