mlsanityData to 14 Oct 2026

ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling

arXiv 2404.16659 ↗First posted 25 Apr 2024cs.CL cs.AIcited by 7

Not on the map. Why?

Abstract (arXiv)

Recently, deep learning-based language models have significantly enhanced text-to-SQL tasks, with promising applications in retrieving patient records within the medical domain. One notable challenge in such applications is discerning unanswerable queries. Through fine-tuning model, we demonstrate the feasibility of converting medical record inquiries into SQL queries. Additionally, we introduce an entropy-based method to identify and filter out unanswerable results. We further enhance result quality by filtering low-confidence SQL through log probability-based distribution, while grammatical and schema errors are mitigated by executing queries on the actual database. We experimentally verified that our method can filter unanswerable questions, which can be widely utilized even when the parameters of the model are not accessible, and that it can be effectively utilized in practice.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

13 of 23 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  5. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  6. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  7. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  8. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  9. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  10. 2020On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translationcited by 127
  11. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  12. 2022How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysiscited by 42
1 more
  1. 2024TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoringcited by 17

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Recordscited by 11
  2. 2025Confidence Estimation for Error Detection in Text-to-SQL Systemscited by 11
  3. 2025LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questionscited by 9
  4. 2026Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Studycited by 3
  5. 2025Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraintscited by 0
  6. 2026CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domaincited by 0
  7. 2026Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agentscited by 0