Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

Large language models can hallucinate confidently, making uncertainty quantification essential for reliable deployment. Existing approaches rely predominantly on token-level signals from the final (unembedding) layer, while the rich geometric structure of intermediate hidden states remains largely unexplored. Aggregating token-level scores to the response level is itself non-trivial, and no single signal reliably catches the confident-but-wrong failure mode where a model commits to every token with high confidence yet produces an incorrect answer. We address this gap by extracting complementary signals from two distinct representational layers: hidden-state geometric complexity (global uncertainty) from the embedding layer, and token-level entropy (local uncertainty) from the unembedding layer. We show empirically that the two signals cover different regimes that are weakly correlated and thus combining them captures failure modes invisible to either alone. Building on this insight, we propose Global Local Uncertainty (GLU), an unsupervised, single-pass framework that fuses the two signals via a multiplicative gate. Experiments across three benchmarks and three model families show that GLU matches or outperforms all unsupervised baselines and remains competitive with supervised methods that lack cross-dataset generalization.