Abstract
This paper presents our approach to the EHRSQL-2024 shared task, which aims to develop a reliable Text-to-SQL system for electronic health records. We propose two approaches that leverage large language models (LLMs) for prompting and fine-tuning to generate EHRSQL queries. In both techniques, we concentrate on bridging the gap between the real-world knowledge on which LLMs are trained and the domain-specific knowledge required for the task. The paper provides the results of each approach individually, demonstrating that they achieve high execution accuracy. Additionally, we show that an ensemble approach further enhances generation reliability by reducing errors. This approach secured us 2nd place in the shared task competition. The methodologies outlined in this paper are designed to be transferable to domain-specific Text-to-SQL problems that emphasize both accuracy and reliability.- Anthology ID:
- 2024.clinicalnlp-1.34
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 360–366
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.34
- DOI:
- Cite (ACL):
- Satya Gundabathula and Sriram Kolar. 2024. PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 360–366, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs (Gundabathula & Kolar, ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.34.pdf