The voting curve from repeated binary predictions is exactly equivalent to a signed voting signature capturing excess latent mass above the majority threshold at binomial variance scales, via signed Hausdorff moments.
Generating with confidence: Uncertainty quantification for black-box large language models.Transactions on Machine Learning Research, 2024
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First-token normalized entropy (phi_first) from one greedy decode reaches mean AUROC 0.820 for hallucination detection, matching or exceeding semantic self-consistency (0.793) and surface self-consistency (0.791) across three 7-8B models and two benchmarks.
citing papers explorer
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When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation
The voting curve from repeated binary predictions is exactly equivalent to a signed voting signature capturing excess latent mass above the majority threshold at binomial variance scales, via signed Hausdorff moments.
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The First Token Knows: Single-Decode Confidence for Hallucination Detection
First-token normalized entropy (phi_first) from one greedy decode reaches mean AUROC 0.820 for hallucination detection, matching or exceeding semantic self-consistency (0.793) and surface self-consistency (0.791) across three 7-8B models and two benchmarks.