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Natural Language Interfaces for Databases

Summary: Text-to-SQL prompt strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks. However, when applied to real-world databases, their performance is significantly less than that reported for these benchmarks, especially for Natural Language (NL) questions that require complex filters and joins to be processed. This presentation first describes a prompt strategy to compile NL questions into SQL queries that incorporates a dynamic few-shot examples strategy and leverages the services provided by a database keyword search (KwS) platform. The presentation details how the precision and recall of the schema-linking process are improved with the help of the examples provided and the keyword-matching service that the KwS platform offers. Then, it shows how the KwS platform can be used to synthesize a view that captures the joins required to process an input NL question and thereby simplify the SQL query compilation step. The presentation concludes with experiments with a real-world relational database to assess the performance of the proposed prompt strategy. The experiments suggest that the prompt strategy achieves an accuracy that surpasses state-of-the-art approaches. Bio: Marco A. Casanova is Full Professor at the Department of Informatics and Coordinator of the Central Planning and Evaluation Office of the Pontifical Catholic University of Rio de Janeiro – PUC-Rio. He graduated in Electronic Engineering at the Military Institute of Engineering (1974), obtained an M.Sc. in Informatics from PUC-Rio (1976), and an M.Sc. (1977) and a Ph.D. (1979) in Applied Mathematics from Harvard University. He was Graduate Program Coordinator (2005-2007) and Director (2007-2011) of the Department of Informatics of PUC-Rio. His research interests concentrate on database conceptual modeling and construction of database management systems. In July 2012, he received the Scientific Merit Award from the Brazilian Computer Society.