Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL
<|reference_start|>Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL: Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their diagnoses. The EHRSQL dataset stands out as a promising benchmark because it is the only dataset that incorporates unanswerable questions in the EHR QA system alongside practical questions. However, in this work, we identify a data bias in these unanswerable questions; they can often be discerned simply by filtering with specific N-gram patterns. Such biases jeopardize the authenticity and reliability of QA system evaluations. To tackle this problem, we propose a simple debiasing method of adjusting the split between the validation and test sets to neutralize the undue influence of N-gram filtering. By experimenting on the MIMIC-III dataset, we demonstrate both the existing data bias in EHRSQL and the effectiveness of our data split strategy in mitigating this bias.<|reference_end|>
@article{yang2024towards,
    title={Towards Unbiased Evaluation of Detecting Unanswerable Questions in
  EHRSQL},
    author={Yongjin Yang, Sihyeon Kim, SangMook Kim, Gyubok Lee, Se-Young Yun,
  Edward Choi},
    journal={arXiv preprint arXiv:2405.01588},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01588},
    primaryClass={cs.CL cs.AI}
}