ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement

2026-03-04

AI Summary

This work proposes ErrorLLM, a novel framework that addresses the challenge of implicit semantic errors in text-to-SQL generation—a problem often overlooked by existing methods—and mitigates the risk of hallucination-induced corruption during error correction. ErrorLLM introduces the first explicit error modeling mechanism tailored for text-to-SQL tasks, leveraging structured features derived from both the natural language question and the database schema. It combines static analysis with specialized error tokens to accurately identify error types and decouples error detection from structural correction. Through error-guided prompt engineering and fine-tuning of large language models, the approach significantly outperforms baseline methods across multiple benchmarks, achieving high F1 scores in error detection while preserving the validity of corrected SQL queries, thereby demonstrating the critical role of precise error identification in effective correction.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Despite the remarkable performance of large language models (LLMs) in text-to-SQL (SQL generation), correctly producing SQL queries remains challenging during initial generation. The SQL refinement task is subsequently introduced to correct syntactic and semantic errors in generated SQL queries. However, existing paradigms face two major limitations: (i) self-debugging becomes increasingly ineffective as modern LLMs rarely produce explicit execution errors that can trigger debugging signals; (ii) self-correction exhibits low detection precision due to the lack of explicit error modeling grounded in the question and schema, and suffers from severe hallucination that frequently corrupts correct SQLs. In this paper, we propose ErrorLLM, a framework that explicitly models text-to-SQL Errors within a dedicated LLM for text-to-SQL refinement. Specifically, we represent the user question and database schema as structural features, employ static detection to identify execution failures and surface mismatches, and extend ErrorLLM's semantic space with dedicated error tokens that capture categorized implicit semantic error types. Through a well-designed training strategy, we explicitly model these errors with structural representations, enabling the LLM to detect complex implicit errors by predicting dedicated error tokens. Guided by the detected errors, we perform error-guided refinement on the SQL structure by prompting LLMs. Extensive experiments demonstrate that ErrorLLM achieves the most significant improvements over backbone initial generation. Further analysis reveals that detection quality directly determines refinement effectiveness, and ErrorLLM addresses both sides by high detection F1 score while maintain refinement effectiveness.

Problem

• text-to-SQL
• SQL refinement
• error modeling
• semantic errors
• large language models

Innovation

• Error Modeling
• Text-to-SQL Refinement
• Error Tokens
• Structural Representation
• Implicit Semantic Errors