33 citations · 42 across the 26 of their papers we have counts for
5 papers · 2 filters
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs
Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou +2
Large Language Models are now key assistants in human decision-making processes. However, a common note always seems to follow: "LLMs can make mistakes. Be careful with important i…
Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
Xiaoou Liu, Tiejin Chen, Longchao Da +3
Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. H…
Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses
Tiejin Chen, Xiaoou Liu, Longchao Da +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks due to large training datasets and powerful transformer architecture. However, the relia…
MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels
Xiaoou Liu, Zhen Lin, Longchao Da +3
Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequ…
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology
Longchao Da, Xiaoou Liu, Jiaxin Dai +3
Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into t…