APPLICATION OF GRAPH DATABASES FOR LANGUAGE MODEL OPTIMIZATION

Abstract

This research paper investigates the application of graph databases in optimizing large language models, focusing on their ability to enhance interpretability, flexibility, and adaptability. By utilizing the inherent properties of graph structures, the study explores how these databases can improve model explainability through the ability to trace the paths of influence, offering clearer insights into model decision-making processes. Another key benefit is the dynamic adjustment of model weights, which provides flexibility for task-specific needs, such as medical diagnostics or personalized recommendations, leading to more accurate outputs.

The study also examines the operational challenges of using graph databases, including slower query processing speeds compared to in-memory computing, significant storage demands, and the complexities of data management. The volume of data required for large language models further exacerbates these issues, raising concerns about the efficiency of storage and access mechanisms. Despite these challenges, the research highlights the potential of graph databases for specific tasks where scalability and speed are less critical, suggesting that they may be best suited for niche applications rather than broader, general-purpose use.

Experimental assessments conducted on the LLaMA 3 model provide valuable insights into the performance of graph databases, including measurements of query processing speed, memory usage, and disk storage requirements. These results are benchmarked against traditional data storage and processing methods, offering a detailed comparison of efficiency and feasibility. Overall, this study offers a comprehensive analysis of the advantages, limitations, and future prospects of using graph databases for large language model optimization, advocating for a balanced approach that addresses both their potential and practical constraints in real-world applications.