Selective Demonstrations for Cross-domain Text-to-SQL
Shuaichen Chang Eric Fosler-Lussier
Large language models (LLMs) with in-context learning have demonstrated
impressive generalization capabilities in the cross-domain text-to-SQL task,
without the use of in-domain annotations. However, incorporating in-domain
demonstration examples has been found to greatly enhance LLMs' performance. In
this paper, we delve into the key factors within in-domain examples that
contribute to the improvement and explore whether we can harness these benefits
without relying on in-domain annotations. Based on our findings, we propose a
demonstration selection framework ODIS which utilizes both out-of-domain
examples and synthetically generated in-domain examples to construct
demonstrations. By retrieving demonstrations from hybrid sources, ODIS
leverages the advantages of both, showcasing its effectiveness compared to
baseline methods that rely on a single data source. Furthermore, ODIS
outperforms state-of-the-art approaches on two cross-domain text-to-SQL
datasets, with improvements of 1.1 and 11.8 points in execution accuracy,
respectively.