Large Language Models for Text-to-SQL: Challenges, Advancements, and Evaluation

Text-to-SQL, translating natural language to SQL, has seen significant advancements due to Large Language Models (LLMs). However, challenges remain in handling complex database schemas, diverse SQL operations beyond simple queries, and natural language ambiguity. To address this, new approaches like MultiSQL and SGU-SQL utilize schema-integrated context, prompt engineering (Chain-of-Thought, decomposition, self-refinement), and graph-based schema linking. Evaluation has also evolved, with new metrics like Enhanced Tree Matching (ETM) and Database State Match being introduced to more accurately assess performance beyond traditional Exact Set Match and Execution Accuracy.