# Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation

**Authors:** Ge Qu, Jinyang Li, Bowen Li, Bowen Qin, Nan Huo, Chenhao Ma, Reynold Cheng  
**arXiv:** [2405.15307](https://arxiv.org/abs/2405.15307)  
**Date:** 2024-05-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2405.15307v1/

## Key points

- Authors address challenges posed by Large Language Models (LLMs) driven by In-Context Learning (ICL) in text-to-SQL tasks
- Common types of hallucinations at each stage of text-to-SQL processing are identified and categorized
- Proposed novel strategy called Task Alignment (TA) leverages experiences from similar tasks to guide LLMs in text-to-SQL generation
- Task Alignment (TA) reduces burden of generalization, helps mitigate hallucinations, and improves overall performance
- Introduction of TA-SQL framework based on Task Alignment strategy
- Experimental results show significant improvements across six models and four mainstream complex text-to-SQL benchmarks
- Potential impact of TA in advancing text-to-SQL generation tasks is highlighted

## Summary

In their paper titled "Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation," authors Ge Qu, Jinyang Li, Bowen Li, Bowen Qin, Nan Huo, Chenhao Ma, and Reynold Cheng address the challenges posed by Large Language Models (LLMs) driven by In-Context Learning (ICL) in text-to-SQL tasks. The authors identify and categorize common types of hallucinations at each stage of text-to-SQL processing to mitigate these issues. They propose a novel strategy called Task Alignment (TA), which leverages experiences from similar tasks to guide LLMs in text-to-SQL generation rather than starting from scratch. Through TA, the burden of generalization is reduced and LLMs are able to effectively mitigate hallucinations and improve overall performance. The authors introduce TA-SQL as a framework based on this strategy and demonstrate its effectiveness through experimental results showing significant improvements across six models and four mainstream complex text-to-SQL benchmarks. This highlights the potential impact of TA in advancing text-to-SQL generation tasks. This work was accepted for presentation at ACL Findings 2024.