surveys-causes-kinds-detection-hallucination-across-data-trainingmechanismsingle paper
Surveys causes, kinds, and detection of hallucination across data, training, and inference, and finds no complete solution.
Capability: Stating false facts confidently
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- Surveys causes, kinds, and detection of hallucination across data, training, and inference, and finds no complete solution.
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 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
- 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
- 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 interventions in 7B-class vision-language models, increasing intervention strength (finer regional evidence partitioning, longer attention-routing windows, stronger textual perturbation) keeps lowering CHAIR object-hallucination scores while producing empty or reduced-coverage responses, so lower hallucination numbers at high intervention strength partly reflect the model saying less rather than grounding better.Grounding answers in the image · unreviewed