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SAGE proposes a group-level uncertainty target that constructs an answer-conditioned uncertainty geometry over sampled responses to improve verbal uncertainty alignment in LLMs, and introduces GUPO for training. Experiments across reasoning tasks show improved uncertainty ranking and reduced overconfidence.
The paper proposes a delegation-based aggregator called Propagational Proxy Voting (PPV) that uses letter entropy and reasoning geometry to improve over majority voting for multi-sample LLM inference, achieving gains on MMLU-Pro without requiring gold labels or auxiliary training.
Researchers introduce SHADE, a hybrid estimator that combines Good-Turing coverage with graph-spectral cues to quantify semantic uncertainty and detect LLM hallucinations when only a few black-box samples are available.