Large Language Model Based Chatbot for Database Interaction through Natural Language

Abstract

In today's data-driven world, the ability to access and interpret information is crucial for informed decision-making. However, technical complexities in database management often create barriers for non-experts. This paper presents a prototype of a chatbot leveraging large language models (LLMs) to bridge this gap. The chatbot interprets natural language questions about structured databases and translates them into SQL queries, retrieving relevant data and converting results back into natural language. The system was tested with four different LLMs, evaluated by human evaluation on a number of metrics: accuracy, relevance, fluency, completeness, cohesion, and naturalness. The results show the usefulness of the approach and how it can make data interaction more user-friendly by democratizing data access, reducing human workload, and empowering users to focus on strategic tasks.