Post-Hoc Conformal Prediction for Hallucination Detection in Vision-Language Models: A Distribution-Free Coverage Guarantee on POPE-Adversarial

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Vision-Language Models (VLMs) frequently produce confident but factually incorrect answers about image content, a failure mode commonly termed hallucination. Standard softmax confidence scores are not calibrated: a model can assign high probability to a wrong answer with no statistical guarantee that this probability reflects the true likelihood of correctness. We apply split-conformal prediction as a post-hoc, model-agnostic wrapper around a frozen LLaVA-1.5-7B model, converting its raw output into a prediction set ({Yes}, {No}, or {Yes, No}) with a distribution-free marginal coverage guarantee: the true label falls inside the set with probability at least 1 − α, for a user-chosen α, under no assumption on the model or data distribution beyond exchangeability. We evaluate on the POPE-Adversarial benchmark (COCO val2014), the hardest of the three standard POPE splits, using 1,000 calibration and 2,000 test samples at a target coverage of 90% (α = 0.10). The calibrated system achieves 91.45% empirical coverage, consistent with the theoretical guarantee, at an average prediction-set size of 1.195 labels: 80.5% of test questions receive a confident singleton set, while 19.5% are flagged as uncertain (both labels retained), directly identifying the subset of answers most likely to be unreliable. Raw point-prediction accuracy on the same data is 83.35%, meaning conformal wrapping does not improve raw accuracy but instead provides calibrated, statistically-grounded uncertainty on top of it. We report the full pipeline, a theoretical sanity check of the observed coverage against its expected sampling distribution, and the limitations of a single-split, single-model evaluation. Code, results and figures: https://github.com/sreejatamaity/Conformal-vlm-hallucination This is a preprint and has not been peer reviewed. AI assistance: Generative AI (Claude, Anthropic) was used for drafting and editing certain sections of the manuscript text and for code debugging. The author conducted all experiments, independently verified all reported results against the raw output files, and takes responsibility for the accuracy and content of the work.