AI RESEARCH
AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation
arXiv CS.CL
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ArXi:2604.06812v1 Announce Type: new Large Language Models (LLMs) have nstrated impressive capabilities in long-form generation, yet their application is hindered by the hallucination problem. While Uncertainty Quantification (UQ) is essential for assessing reliability, the complex structure makes reliable aggregation across heterogeneous themes difficult, in addition, existing methods often overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition.