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.
Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, and Csaba Szepesvári
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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.