4 papers
Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks
Kevin Wang, Subre Abdoul Moktar, Jia Li +2
Large Language Models (LLMs) have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramou…
Evidential Uncertainty Probes for Graph Neural Networks
Linlin Yu, Kangshuo Li, Pritom Kumar Saha +2
Accurate quantification of both aleatoric and epistemic uncertainties is essential when deploying Graph Neural Networks (GNNs) in high-stakes applications such as drug discovery an…
Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function
Linlin Yu, Bowen Yang, Tianhao Wang +2
The fusion of raw sensor data to create a Bird's Eye View (BEV) representation is critical for autonomous vehicle planning and control. Despite the growing interest in using deep l…
Federated In-Context LLM Agent Learning
Panlong Wu, Kangshuo Li, Junbao Nan +1
Large Language Models (LLMs) have revolutionized intelligent services by enabling logical reasoning, tool use, and interaction with external systems as agents. The advancement of L…