3 papers
cs.AI2026
Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification
Yanni Dong, Minghua Liu, Meiling Zhu +3
Large Language Models often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applications. Existing uncertainty metr…
cs.CL2025
SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG
Xiaonan Si, Meilin Zhu, Simeng Qin +7
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but are vulnerable to corpus poisoning and contamination attacks, which ca…
cs.AI2025
Shapley Uncertainty in Natural Language Generation
Meilin Zhu, Gaojie Jin, Xiaowei Huang +1
In question-answering tasks, determining when to trust the outputs is crucial to the alignment of large language models (LLMs). Kuhn et al. (2023) introduces semantic entropy as a…