VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and imperfect reasoning reliability. As a result, careful inspection of model behavior is essential for assessing whether diagnostic reasoning is reliable and clinically grounded. However, debugging medical LLMs remains difficult. First, developers often lack sufficient medical domain expertise to interpret model errors in clinically meaningful terms. Second, models can fail across a large and diverse set of instances involving different input types, tasks, and reasoning steps, making it challenging for developers to prioritize which errors de...
| Authors | Yurui Xiang, Xingyi Mao, Rui Sheng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 April 2026 |
| Last updated at source | 27 July 2026 |
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