Table selection using information retrieval techniques for table-agnostic Text-to-SQL

Abstract

Text-to-SQL has been effectively addressed using various NLP approaches, enabling the translation of natural language queries into SQL queries. A common prerequisite for these implementations, however, is the availability of the database table during inference. This requirement can pose challenges in scenarios where the table is not readily accessible to users. This work is motivated by the ongoing development of a chatbot tool within a private company, aimed at streamlining database interactions for the users. To address the table accessibility limitation, this study leverages Information Retrieval techniques to implement table selection based solely on the natural language query. We finetune pre-trained models like BERT and GIST-NoInstruct using the ColBERT method. We train our models using data we curate in-house by employing established LLM-prompting techniques. We prepare individual training datasets using two negative sampling techniques: uniform distribution and weighted probability distribution. We also experiment with various data fusion techniques such as RRF, CombMNZ, and Linear Combination to combine results from multiple search strategies. Our approach outperforms baseline methods in table retrieval, while also providing a comparative analysis of various retrieval strategies.