GLU is a single-pass unsupervised uncertainty score for LLMs formed by multiplying global hidden-state geometric entropy with local token entropy, shown to match or beat baselines on three model families and six benchmarks while catching failure modes local signals miss.
AA-Omniscience: Evaluating cross-domain knowledge reliability in large language models.arXiv preprint arXiv:2511.13029,
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Integrating Local and Global Entropy for Uncertainty Quantification in LLMs
GLU is a single-pass unsupervised uncertainty score for LLMs formed by multiplying global hidden-state geometric entropy with local token entropy, shown to match or beat baselines on three model families and six benchmarks while catching failure modes local signals miss.