black-box-hallucination-detection-using-only-sampled-responses-token-log-probabilitiesFor black-box hallucination detection using only sampled responses and token log-probabilities, semantic-entropy signals go to zero whenever the samples collapse into one semantic cluster — which happens for a large share of hallucinated queries, up to nearly all of them on tasks with confusable entities and figures — while token-level uncertainty still separates many of those cases, so the two signals fail on different queries and combining them detects more than either alone.
Ingested from a paper but not yet reviewed by a human. It is deliberately inert: it does not move any technique’s standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Read the source before relying on it.
Capability: Stating false facts confidently
Observed on
Black-box API access exposing text and token log-probabilities, no trusted reference document; measured on seven QA/extraction/summary benchmarks with four models. Supervised combi.
Sources
- Measured: single-cluster rate among hallucinated queries ranged 39% (AmbigQA) to 99% (Financial Summaries); median TopK uncertainty higher for hallucinated single-cluster queries on six of seven datasets, though 21-56% of them fell below the non-hallucinated median. Stacked led or tied in 11 of 26 model-dataset comparisons and was within 0.05 AUROC of the best in 20 of 26. Authors note the leading method's confidence interval overlaps a competitor on every dataset, so rankings among methods are not conclusive; the complementarity analysis is stronger than the ranking.
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Related claims
- For closed-book hallucination detection, training a small supervised classifier over many heterogeneous uncertainty scores beats the best single score, but the gain comes from the diversity of sampling-based black-box signals: ensembles over only single-generation token-probability features add little, while black-box-only ensembles do nearly as well as the full set.Stating false facts confidently · unreviewed
- For training-free inference-time hallucination mitigation in 7B vision- language models, reported gains on object-hallucination benchmarks are largely inseparable from reduced informativeness: hallucination rate and object recall/coverage move together, so a lower score can mean the model mentioned fewer visual entities rather than grounded better.Whether the measurement made the finding · unreviewed
- A ~2M-parameter classifier head that reads hidden states already computed during generation (tapping shallow, middle and deep layers of the generating model) detects response-level hallucination at least as well as specialized hidden-state hallucination detectors, and its detection quality rises with the scale of the base model it is attached to.Stating false facts confidently · unreviewed
- In diffusion vision-language models generating free-form captions, object tokens that are hallucinated commit at later denoising steps and with lower per-token confidence than grounded object tokens, so the commit-step trajectory is an available internal hallucination signal that has no counterpart in autoregressive decoding.Grounding answers in the image · unreviewed
- In synthetic hallucination benchmarks where faithful items are human-written and hallucinated items are LLM-rewrites, a detector's binary score partly measures human-vs-machine style rather than factuality: feeding faithful LLM rewrites of the same truthful articles makes some fine-tuned encoders flag nearly all of them as hallucinated.Whether the measurement made the finding · unreviewed
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Show that on single-semantic-cluster hallucinated queries token-uncertainty features give no separation above chance, or that a combined detector never beats the better single signal across datasets and models. Proposed technique, not catalogued: Stacked semantic-plus-token hallucination detector.