Summary of Probgate at Ehrsql 2024: Enhancing Sql Query Generation Accuracy Through Probabilistic Threshold Filtering and Error Handling, by Sangryul Kim et al.
ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling
by Sangryul Kim, Donghee Han, Sehyun Kim
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recently, advancements in deep learning-based language models have revolutionized text-to-SQL tasks, with significant applications in retrieving patient records within the medical domain. Specifically, fine-tuning models for this task enables effective conversion of medical record inquiries into SQL queries. A notable challenge in these applications is identifying unanswerable queries. To address this issue, we propose an entropy-based method to filter out such results, leveraging log probability-based distribution to enhance result quality and mitigate grammatical and schema errors by executing queries on the actual database. Our experimental results demonstrate that our method can effectively filter unanswerable questions, even when model parameters are not accessible, with implications for practical utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a huge library of medical records, but you need to find specific information quickly. Computers can help by understanding natural language and translating it into a special language that databases understand. However, sometimes the computers might get stuck because they don’t know how to answer certain questions. A team of researchers has developed a way to identify these unanswerable questions and filter them out so you only get useful results. This is done by analyzing the computer’s confidence in its answers and making sure it doesn’t make mistakes that would waste time. The team tested their method and found it works well, even when they can’t access all the details of how the computer works. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Probability
