Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che
Abstract
In-context learning with large language models (LLMs) is the current mainstream method for text-to-SQL. Previous studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs. In this work, we address measuring and enhancing the diversity of the text-to-SQL demonstration pool. First, we introduce a diversity metric and present that the diversity of the existing labeling data can be further enhanced. Motivated by these findings, we propose Fused that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs. Fused achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on several mainstream datasets, demonstrating its effectiveness.- Anthology ID:
- 2024.findings-emnlp.65
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1193–1207
- Language:
- URL:
- https://aclanthology.org/2024.findings-emnlp.65
- DOI:
- 10.18653/v1/2024.findings-emnlp.65
- Cite (ACL):
- Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, and Wanxiang Che. 2024. Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 1193–1207, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL (Wang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.findings-emnlp.65.pdf