@inproceedings{tai-etal-2023-exploring,
title = "Exploring Chain of Thought Style Prompting for Text-to-{SQL}",
author = "Tai, Chang-Yu and
Chen, Ziru and
Zhang, Tianshu and
Deng, Xiang and
Sun, Huan",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.327",
doi = "10.18653/v1/2023.emnlp-main.327",
pages = "5376--5393",
abstract = "In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs{'} reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting and least-to-most prompting. Our experiments demonstrate that iterative prompting as in least-to-most prompting may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method.",
}
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<abstract>In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs’ reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting and least-to-most prompting. Our experiments demonstrate that iterative prompting as in least-to-most prompting may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method.</abstract>
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%0 Conference Proceedings
%T Exploring Chain of Thought Style Prompting for Text-to-SQL
%A Tai, Chang-Yu
%A Chen, Ziru
%A Zhang, Tianshu
%A Deng, Xiang
%A Sun, Huan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F tai-etal-2023-exploring
%X In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs’ reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting and least-to-most prompting. Our experiments demonstrate that iterative prompting as in least-to-most prompting may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method.
%R 10.18653/v1/2023.emnlp-main.327
%U https://aclanthology.org/2023.emnlp-main.327
%U https://doi.org/10.18653/v1/2023.emnlp-main.327
%P 5376-5393
Markdown (Informal)
[Exploring Chain of Thought Style Prompting for Text-to-SQL](https://aclanthology.org/2023.emnlp-main.327) (Tai et al., EMNLP 2023)
ACL
- Chang-Yu Tai, Ziru Chen, Tianshu Zhang, Xiang Deng, and Huan Sun. 2023. Exploring Chain of Thought Style Prompting for Text-to-SQL. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5376–5393, Singapore. Association for Computational Linguistics.