LLM Based Text to SQL

Large language models (LLMs) are revolutionizing text-to-SQL, the task of automatically translating natural language queries into structured SQL code for database interaction. Current research focuses on improving accuracy and efficiency through techniques like in-context learning, fine-tuning, and multi-agent collaborative frameworks that leverage both symbolic and semantic reasoning, often incorporating external knowledge sources or retrieval mechanisms to enhance performance. This advancement significantly impacts database accessibility, enabling more intuitive and efficient data querying for both technical and non-technical users across diverse domains, such as hardware design and general data analysis.