Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning

2026-06-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

CoTE-SQL is a new approach designed to improve text-to-SQL translation by large language models (LLMs), addressing their common struggle with balancing reasoning and generalization. This method introduces three key innovations: self-enhanced reasoning traces derived from LLMs without human annotation, structured chain-of-thought (CoT) prompting that uses modular decomposition and example retrieval, and error-aware revision based on SQL execution feedback. Evaluated on the Spider and Bird benchmarks, CoTE-SQL achieves new leading performance among open-source LLMs of comparable sizes on Bird, with 53.39% EX and 59.02 VES, and strong results on Spider, reaching 79.60% EX and 77.19 VES. The system shows particularly significant gains on complex queries, demonstrating the effectiveness of its combined self-enhancement, structured reasoning, and execution-time feedback framework.

Key takeaway

For Machine Learning Engineers developing text-to-SQL solutions, CoTE-SQL offers a robust framework to overcome common LLM limitations in reasoning and generalization. You should consider integrating self-enhanced reasoning traces, structured chain-of-thought prompting, and execution-time feedback into your LLM fine-tuning process. This approach can significantly improve performance on complex queries, as demonstrated by its leading results on benchmarks like Bird and Spider.

Key insights

CoTE-SQL enhances LLM-based text-to-SQL by integrating self-enhanced reasoning, structured CoT prompting, and execution feedback for improved performance.

Principles

• Self-enhancement can distill reasoning traces from LLMs.
• Structured CoT prompting improves complex query handling.
• Execution feedback is crucial for error-aware revision.

Method

CoTE-SQL employs self-enhanced reasoning traces, structured chain-of-thought prompting with modular decomposition and example retrieval, and error-aware revision using SQL execution feedback to generate and refine SQL queries.

In practice

• Implement self-enhanced reasoning for LLM fine-tuning.
• Design CoT prompts with modularity and example retrieval.
• Integrate SQL execution feedback for automated error correction.

Topics

• Text-to-SQL
• Large Language Models
• Chain-of-Thought Prompting
• Self-Enhanced Fine-Tuning
• SQL Query Generation
• Model Generalization

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• TAHOE: Text-to-SQL with Automated Hint Optimization from Experience — TAHOE optimizes Text-to-SQL LLM prompts by dynamically learning and applying error-driven hints from development and deployment experiences.
• Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning — CoT-PoT ensembling improves LLM reasoning accuracy and efficiency by leveraging diverse, complementary reasoning modalities.
• Teaching Your SQL Generator to Fix Its Own Mistakes — LLMs can self-correct runtime SQL errors by processing database feedback in a retry loop.
• Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs — Code-on-Graph integrates LLMs with KGs using programmatic reasoning, representing schemas as Python classes for flexible, scalable knowledge utilization.
• Language Effects in Text-to-SQL Across English and Portuguese — Multilingual Text-to-SQL accuracy drops significantly from English to Portuguese, even with advanced LLMs.
• Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data — Base LLMs inherently predict external judge scores, a latent ability efficiently elicited by Self-Evaluation Elicitation (SEE) with minimal data.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.