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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supportsA Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Surveys causes, kinds, and detection of hallucination across data, training, and inference, and finds no complete solution.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

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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 · reviewed by AI
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 · reviewed by AI
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 · reviewed by AI
In 7B-class LVLMs (LLaVA-1.5, MiniGPT-4, mPLUG-Owl2), hallucination directions in activation space are not a single mode: clustering hallucinated-minus-faithful hidden-state differences into several low-rank subspaces and weighting them per test image lowers object-hallucination rates slightly below a single global null-space projection, with the margin small (CHAIR_I 5.30 to 4.92 on LLaVA-1.5) and inconsistent on yes/no existence probing.Grounding answers in the image · reviewed by AI
For open-ended emotion interpretation by multimodal LLMs, coarse-grained hallucination mitigations designed for object hallucination (VCD, ICD, M3ID) and self-verification (EmotionHallucer) cut unsupported claims in some facets while raising them in others — e.g. lowering audio hallucination but increasing expression and action hallucination — so a single aggregate hallucination rate hides the trade-off.Stating false facts confidently · reviewed by AI