Abstract
Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy. Closing the performance gap between small open-source models and large proprietary models is crucial to mitigate this reliance. To this end, we introduce a novel two-stage fine-tuning approach that decomposes the task into two simpler tasks. Through comprehensive evaluation on three large cross-domain datasets and two small LLMs, we show that this approach improves execution accuracy by 3 to 7 percent, effectively aligning the performance of open-source models with their proprietary counterparts. Our proposed method has achieved 60.31% execution accuracy on Bird hold-out test set, which is the highest performance among methods using 7B parameter models.- Anthology ID:
- 2024.findings-emnlp.481
- 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:
- 8212–8220
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.481/
- DOI:
- 10.18653/v1/2024.findings-emnlp.481
- Cite (ACL):
- Mohammadreza Pourreza and Davood Rafiei. 2024. DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8212–8220, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models (Pourreza & Rafiei, Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.481.pdf