closed-book-hallucination-detection-training-small-supervised-classifier-over-manyFor 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.
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
Four closed-weight LLMs (Gemini 2.5 Flash/Pro, GPT-4o, GPT-4o-mini), nine datasets, short-form QA, claim-level long-form, and Python competitive-programming code; labels available .
Sources
- AUROC over 25 stratified splits per setting; best ensemble beat the best individual scorer in 30 of 32 settings and had lowest ECE in 29 of 32, with gains from ~100 labeled instances; ablation on five short-form datasets: black-box-only ensembles beat the best black- box scorer in 19 of 20 settings, white-box-only in 11 of 20. The single-scorer baseline is selected on the test set, so it is optimistic. Sample-efficiency finding replicates Bakman et al. Margins are often 0.01-0.02 AUROC and sometimes inside confidence intervals; [truncated]
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- For 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.Stating false facts confidently · 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
- For detecting hallucinated text spans in vision-language model outputs, span localization and confidence calibration come from different architectures: a fine-tuned generative VLM proposes better span boundaries but scores them poorly, while a discriminative token tagger trained with a direct regression loss on human-consensus confidence gives better-correlated probabilities but conservative spans, so using the generative spans as proposals and the tagger's probabilities as scores beats either alone.Stating false facts confidently · 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
- 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
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: An evaluation where an ensemble restricted to single-generation white-box token-probability features matches full or black-box ensembles, or where the full ensemble fails to beat a validation-selected single scorer in most settings. Proposed technique, not catalogued: Supervised ensemble of uncertainty-quantification scores.