The paper "Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation" addresses the issue of hallucinations in large language models (LLMs) used for text-to-SQL conversion. The authors identify and categorize common types of hallucinations at each stage of the text-to-SQL process, including schema-based and logic-based hallucinations. They propose a novel strategy called Task Alignment (TA), which aims to mitigate hallucinations by encouraging LLMs to leverage experiences from similar tasks rather than starting from scratch. This strategy is integrated into a new framework called TA-SQL, which consists of a Task-Aligned Schema Linking (TASL) module and a Task-Aligned Logical Synthesis (TALOG) module. Experimental results on four benchmark datasets demonstrate the effectiveness of TA-SQL, showing a significant improvement in execution accuracy compared to the GPT4 baseline. The framework is also shown to be model-agnostic, working effectively with both closed-source and open-source LLMs. The paper provides a comprehensive analysis of the importance of hallucination mitigation in text-to-SQL systems and suggests promising directions for future research.The paper "Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation" addresses the issue of hallucinations in large language models (LLMs) used for text-to-SQL conversion. The authors identify and categorize common types of hallucinations at each stage of the text-to-SQL process, including schema-based and logic-based hallucinations. They propose a novel strategy called Task Alignment (TA), which aims to mitigate hallucinations by encouraging LLMs to leverage experiences from similar tasks rather than starting from scratch. This strategy is integrated into a new framework called TA-SQL, which consists of a Task-Aligned Schema Linking (TASL) module and a Task-Aligned Logical Synthesis (TALOG) module. Experimental results on four benchmark datasets demonstrate the effectiveness of TA-SQL, showing a significant improvement in execution accuracy compared to the GPT4 baseline. The framework is also shown to be model-agnostic, working effectively with both closed-source and open-source LLMs. The paper provides a comprehensive analysis of the importance of hallucination mitigation in text-to-SQL systems and suggests promising directions for future research.