3 papers
cs.AI2025
DiagnoLLM: A Hybrid Bayesian Neural Language Framework for Interpretable Disease Diagnosis
Bowen Xu, Xinyue Zeng, Jiazhen Hu +2
Building trustworthy clinical AI systems requires not only accurate predictions but also transparent, biologically grounded explanations. We present \texttt{DiagnoLLM}, a hybrid fr…
cs.CL2025
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Tuo Wang, Adithya Kulkarni, Tyler Cody +3
Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semanti…
cs.LG2025
Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks
Tuo Wang, Jian Kang, Yujun Yan +2
Conformal prediction for graph neural networks (GNNs) offers a promising framework for quantifying uncertainty, enhancing GNN reliability in high-stakes applications. However, exis…