mlsanityData to 14 Oct 2026

LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs

arXiv 2405.11162 ↗First posted 18 May 2024cs.CLcited by 7

On the map: Synthetic data and self-improvement for LLMs → Medical synthetic data and agents

Abstract (arXiv)

Text-to-SQL models are pivotal for making Electronic Health Records (EHRs) accessible to healthcare professionals without SQL knowledge. With the advancements in large language models, these systems have become more adept at translating complex questions into SQL queries. Nonetheless, the critical need for reliability in healthcare necessitates these models to accurately identify unanswerable questions or uncertain predictions, preventing misinformation. To address this problem, we present a self-training strategy using pseudo-labeled unanswerable questions to enhance the reliability of text-to-SQL models for EHRs. This approach includes a two-stage training process followed by a filtering method based on the token entropy and query execution. Our methodology's effectiveness is validated by our top performance in the EHRSQL 2024 shared task, showcasing the potential to improve healthcare decision-making through more reliable text-to-SQL systems.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Medical synthetic data and agents, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 21 of its 30 references that are in the map. First posted on arXiv 18 May 2024.

Builds on

21 of 30 references.

References · 21

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  3. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  4. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  6. 2023StarCoder: may the source be with you!cited by 799
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  8. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  9. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  10. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  11. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
  12. 2018emrQA: A Large Corpus for Question Answering on Electronic Medical Recordscited by 135
9 more
  1. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  2. 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
  3. 2022Self-Training: A Surveycited by 86
  4. 2019Text-to-SQL Generation for Question Answering on Electronic Medical Recordscited by 77
  5. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  6. 2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 45
  7. 2020Hybrid Ranking Network for Text-to-SQLcited by 33
  8. 2020Knowledge Graph-based Question Answering with Electronic Health Recordscited by 19
  9. 2024Retrieval-Augmented Data Augmentation for Low-Resource Domain Taskscited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2024PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQLcited by 7
  2. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  3. 2026Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Studycited by 3
  4. 2025EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databasescited by 0
  5. 2025Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraintscited by 0
  6. 2025Query Carefully: Detecting the Unanswerables in Text-to-SQL Taskscited by 0
  7. 2026CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domaincited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answering
  2. 2025UTSA-NLP at ArchEHR-QA 2025: Improving EHR Question Answering via Self-Consistency Prompting
  3. 2024Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records
  4. 2025Confidence Estimation for Error Detection in Text-to-SQL Systems
  5. 2026EHR-RobustGym: Benchmarking and Training Agents for Robust Clinical Reasoning
  6. 2026Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models
  7. 2025SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
  8. 2025EHRStruct: A Comprehensive Benchmark Framework for Evaluating Large Language Models on Structured Electronic Health Record Tasks