Leveraging LLM-Generated Schema Descriptions for Unanswerable Question Detection in Clinical Data
Donghee Han, Seungjae Lim, Daeyoung Roh, Sangryul Kim, Sehyun Kim, Mun Yong Yi
Abstract
Recent advancements in large language models (LLMs) have boosted research on generating SQL queries from domain-specific questions, particularly in the medical domain. A key challenge is detecting and filtering unanswerable questions. Existing methods often relying on model uncertainty, but these require extra resources and lack interpretability. We propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. Furthermore, we found that LLM-generated schema descriptions can significantly enhance the prediction accuracy. Our method provides a resource-efficient solution for unanswerable question detection in domain-specific question answering systems.
BibTeX
@inproceedings{han-etal-2025-leveraging,
title = "Leveraging {LLM}-Generated Schema Descriptions for Unanswerable Question Detection in Clinical Data",
author = "Han, Donghee and
Lim, Seungjae and
Roh, Daeyoung and
Kim, Sangryul and
Kim, Sehyun and
Yi, Mun Yong",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.706/",
pages = "10594--10601"
}