mlsanityData to 13 Oct 2026

PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs

arXiv 2405.08839 ↗First posted 14 May 2024cs.DB cs.AI cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 14 May 2024.

Builds on

13 of 17 references.

References · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,133
  3. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,001
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  5. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 680
  6. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 472
  7. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 422
  8. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 408
  9. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 163
  10. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  11. 2022MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsingcited by 22
  12. 2022STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsingcited by 19
1 more
  1. 2023Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chaincited by 12

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraintscited by 0
  2. 2026CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domaincited by 0