Abstract
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.
Abstract (translated)
近年来，基于深度学习的语言模型在文本到数据库任务上取得了显著的提高，在医疗领域有广泛的应用，如检索病历记录。在这种应用中，一个值得注意的是区分不可回答的问题。通过微调模型，我们证明了将医疗记录查询转换为SQL查询是可能的。此外，我们还引入了一种基于熵的方法来识别和过滤不可回答的结果。通过基于日志概率分布过滤低置信度的SQL，我们进一步提高了结果的质量。通过在实际数据库上执行查询来过滤低置信度的SQL，我们可以缓解语义和模式错误。我们通过实验验证，我们的方法可以过滤不可回答的问题，即使模型的参数不可用，实际应用中也可以广泛使用，而且在实践中取得了良好的效果。
URL
https://arxiv.org/abs/2404.16659
