  Large Language Models (LLMs) driven by In-Context Learning (ICL) have
significantly improved the performance of text-to-SQL. Previous methods
generally employ a two-stage reasoning framework, namely 1) schema linking and
2) logical synthesis, making the framework not only effective but also
interpretable. Despite these advancements, the inherent bad nature of the
generalization of LLMs often results in hallucinations, which limits the full
potential of LLMs. In this work, we first identify and categorize the common
types of hallucinations at each stage in text-to-SQL. We then introduce a novel
strategy, Task Alignment (TA), designed to mitigate hallucinations at each
stage. TA encourages LLMs to take advantage of experiences from similar tasks
rather than starting the tasks from scratch. This can help LLMs reduce the
burden of generalization, thereby mitigating hallucinations effectively. We
further propose TA-SQL, a text-to-SQL framework based on this strategy. The
experimental results and comprehensive analysis demonstrate the effectiveness
and robustness of our framework. Specifically, it enhances the performance of
the GPT-4 baseline by 21.23% relatively on BIRD dev and it yields significant
improvements across six models and four mainstream, complex text-to-SQL
benchmarks.
