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Breaking the Language Barrier: Natural Language to SQL Using Large Language Models

Author(s): Suvojit Hore, Akshit Jain, Maninder Kaur, Kushal Singhal, Trimith Chatterjee, Shashank Shekhar, Sheenam Kumar, Vivek Sharma, Ramesh Kumar, Shubham Agarwal

Traditionally retailers employ a strategic integration of digital screens and printed media in hypermarkets to captivate customers, convey brand messaging, and increase sales of their products. During these media campaigns, vast amounts of product transaction data are recorded that require extensive analysis, comparison, and the ability to quickly export specific data for non-technical media planners to be able to visualize, understand, and plan media campaigns more effectively.

This paper introduces an innovative approach to building a chatbot interface for the transformation of natural language into SQL queries by utilizing the large language model NSQL 350M, which can be used to perform select operations on databases to retrieve and analyze specific data. This enables media planners to ask the chatbot any query about their historical campaign data in English, and the chatbot can translate that into an SQL Query which is executed on the database, thereby retrieving the necessary information.

The paper emphasizes the process of prompt engineering and finetuning the language model to ensure its accuracy is up to the mark and language model hallucination is minimal, and it highlights the potential of the chatbot in several applications for retail media campaigns.

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