AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation
Large Language Models (LLMs) have demonstrated 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. To address these challenges, we propose AGSC (Adaptive Granularity and GMM-based Semantic Clustering), a UQ framework tailored for long-form generation. AGSC first uses NLI neutral probabilities as triggers to disting...
| Authors | Guanran Luo, Wentao Qiu, Wanru Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 April 2026 |
| Last updated at source | 14 April 2026 |
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