The First Token Knows: Single-Decode Confidence for Hallucination Detection
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| Authors | Mina Gabriel |
| Year | 2026 |
| HF Upvotes | 1 |
| arXiv | 2605.05166 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Self-consistency detects hallucinations by generating multiple sampled answers to a question and measuring agreement, but this requires repeated decoding and can be sensitive to lexical variation. Semantic self-consistency improves this by clustering sampled answers by meaning using natural language inference, but it adds both sampling cost and external inference overhead. We show that first-token confidence, phi_first, computed from the normalized entropy of the top-K logits at the first content-bearing answer token of a single greedy decode, matches or modestly exceeds semantic self-consistency on closed-book short-answer factual question answering. Across three 7-8B instruction-tuned models and two benchmarks, phi_first achieves a mean AUROC of 0.820, compared with 0.793 for semantic agreement and 0.791 for standard surface-form self-consistency. A subsumption test shows that phi_first is moderately to strongly correlated with semantic agreement, and combining the two signals yields only a small AUROC improvement over phi_first alone. These results suggest that much of the uncertainty information captured by multi-sample agreement is already available in the model's initial token distribution. We argue that phi_first should be reported as a default low-cost baseline before invoking sampling-based uncertainty estimation.
Engineering Breakdown
Plain English
This paper proposes a fast hallucination detection method that uses only the confidence of the first token in a model's response, rather than generating multiple outputs and comparing them. The method achieves 0.820 AUROC across three 7-8B models, matching or slightly beating semantic self-consistency (0.793 AUROC) while requiring just a single forward pass instead of multiple samples.
Key Engineering Insight
You can detect whether a model is hallucinating by looking at the normalized entropy of the top-K logits at just the first content token of a greedy decode. This single-pass signal captures model uncertainty as well as expensive multi-sample consensus methods do.
Why It Matters for Engineers
Hallucination detection in production systems today requires either running inference multiple times (expensive, latency-killer) or maintaining separate fact-checking pipelines (engineering overhead). A signal that works in a single forward pass with no extra sampling or external models means you can add hallucination detection to every inference with minimal cost.
Research Context
Prior work used multi-sample self-consistency or semantic clustering to catch hallucinations, both expensive at scale. This paper shows that first-token confidence—information already available during greedy decoding at no extra cost—is a surprisingly effective proxy for model confidence. This opens the door to built-in, zero-overhead hallucination detection in production deployments.
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