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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Selective Demonstrations for Cross-domain Text-to-SQL
%A Chang, Shuaichen
%A Fosler-Lussier, Eric
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chang-fosler-lussier-2023-selective
%X 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.
%R 10.18653/v1/2023.findings-emnlp.944
%U https://aclanthology.org/2023.findings-emnlp.944
%U https://doi.org/10.18653/v1/2023.findings-emnlp.944
%P 14174-14189

Markdown (Informal)

[Selective Demonstrations for Cross-domain Text-to-SQL](https://aclanthology.org/2023.findings-emnlp.944) (Chang & Fosler-Lussier, Findings 2023)

• Selective Demonstrations for Cross-domain Text-to-SQL (Chang & Fosler-Lussier, Findings 2023)

ACL

• Shuaichen Chang and Eric Fosler-Lussier. 2023. Selective Demonstrations for Cross-domain Text-to-SQL. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14174–14189, Singapore. Association for Computational Linguistics.