Recent advancements in large language models(LLMs)have significantly contributed to the progress of the Text-to-SQL task.A common requirement in many of these works is the post-correction of SQL queries.However,the ma...Recent advancements in large language models(LLMs)have significantly contributed to the progress of the Text-to-SQL task.A common requirement in many of these works is the post-correction of SQL queries.However,the majority of this process entails analyzing error cases to develop prompts with rules that eliminate model bias.And there is a weakness of execution verification for SQL queries.In addition,the prevalent techniques primarily depend on GPT-4 and few-shot prompts,resulting in expensive costs.To investigate the effective methods for SQL refinement in a cost-efficient manner,we introduce Semantic-Enhanced Text-to-SQL with Adaptive Refinement(SEA-SQL),which includes Adaptive Bias Elimination and Dynamic Execution Adjustment,aims to improve performance while minimizing resource expenditure with zero-shot prompts.Specifically,SEA-SQL employs a semantic-enhanced schema to augment database information and optimize SQL queries.During the SQL query generation,a fine-tuned adaptive bias eliminator is applied to mitigate inherent biases caused by the LLM.The dynamic execution adjustment is utilized to guarantee the executability of the bias eliminated SQL query.We conduct experiments on the Spider and BIRD datasets to demonstrate the effectiveness of this framework.The results demonstrate that SEA-SQL achieves state-of-the-art performance in the GPT-3.5 scenario with 9%-58% of the generation cost.Furthermore,SEA-SQL is comparable to GPT-4 with only 0.9%-5.3% of the generation cost.Our code is available at the website of github.com/545999961/SEA-SQL.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62272054,62192784,62172056)the Beijing Nova Program(No.20230484319)the Xiaomi Young Talents Program.
文摘Recent advancements in large language models(LLMs)have significantly contributed to the progress of the Text-to-SQL task.A common requirement in many of these works is the post-correction of SQL queries.However,the majority of this process entails analyzing error cases to develop prompts with rules that eliminate model bias.And there is a weakness of execution verification for SQL queries.In addition,the prevalent techniques primarily depend on GPT-4 and few-shot prompts,resulting in expensive costs.To investigate the effective methods for SQL refinement in a cost-efficient manner,we introduce Semantic-Enhanced Text-to-SQL with Adaptive Refinement(SEA-SQL),which includes Adaptive Bias Elimination and Dynamic Execution Adjustment,aims to improve performance while minimizing resource expenditure with zero-shot prompts.Specifically,SEA-SQL employs a semantic-enhanced schema to augment database information and optimize SQL queries.During the SQL query generation,a fine-tuned adaptive bias eliminator is applied to mitigate inherent biases caused by the LLM.The dynamic execution adjustment is utilized to guarantee the executability of the bias eliminated SQL query.We conduct experiments on the Spider and BIRD datasets to demonstrate the effectiveness of this framework.The results demonstrate that SEA-SQL achieves state-of-the-art performance in the GPT-3.5 scenario with 9%-58% of the generation cost.Furthermore,SEA-SQL is comparable to GPT-4 with only 0.9%-5.3% of the generation cost.Our code is available at the website of github.com/545999961/SEA-SQL.
