ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL

The article introduces ORANGE, an innovative framework designed to enhance the translation of natural language into SQL queries by incorporating domain knowledge specific to databases. While large language models have made significant advancements in natural language processing, they still face challenges in fully capturing the semantic nuances required for accurate database querying. ORANGE addresses this gap by leveraging historical translation logs, which provide insights into real-world database usage patterns, thereby improving the framework’s contextual understanding. This approach aims to bridge the semantic divide that persists in current text-to-SQL systems, enabling more precise and reliable query generation. By focusing on domain-specific knowledge and practical usage data, ORANGE represents a targeted effort to refine the interaction between natural language inputs and structured database queries. The framework’s development reflects ongoing research efforts to build upon the strengths of large language models while mitigating their limitations in specialized contexts. Overall, ORANGE exemplifies a promising direction in the evolution of AI-driven database querying tools.