Introduces VidPair-Halluc benchmark of 1K background-controlled adversarial video pairs and 11K QA pairs generated via PairFlow pipeline to evaluate hallucination in LVMs.
Science China Information Sciences67(12), 220105 (2024)
6 Pith papers cite this work. Polarity classification is still indexing.
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DO-Bench is a controlled benchmark that attributes VLM object hallucination errors to textual prior pressure, perceptual limits, or their interaction via two diagnostic dimensions and metrics.
DeP mitigates MLLM hallucinations by dynamically perturbing text prompts to identify and reinforce stable visual evidence regions while counteracting language prior biases using attention variance and logit statistics.
QA-SNNE adds question-answer alignment via bilateral gating to semantic nearest neighbor entropy, yielding higher AUROC for uncertainty detection in surgical VQA models under both standard and rephrased questions.
ESC uses emotional cues triggered by an external verifier to enable training-free self-correction in VLMs, improving reliability on safety, hallucination, and reasoning benchmarks.
VLMs fine-tuned on a consistency-probed Visual-Idk dataset via SFT and preference optimization raise truthful rate from 57.9% to 67.3% and show internal evidence of genuine boundary recognition.
citing papers explorer
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No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs
Introduces VidPair-Halluc benchmark of 1K background-controlled adversarial video pairs and 11K QA pairs generated via PairFlow pipeline to evaluate hallucination in LVMs.
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DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
DO-Bench is a controlled benchmark that attributes VLM object hallucination errors to textual prior pressure, perceptual limits, or their interaction via two diagnostic dimensions and metrics.
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Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation
DeP mitigates MLLM hallucinations by dynamically perturbing text prompts to identify and reinforce stable visual evidence regions while counteracting language prior biases using attention variance and logit statistics.
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When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA
QA-SNNE adds question-answer alignment via bilateral gating to semantic nearest neighbor entropy, yielding higher AUROC for uncertainty detection in surgical VQA models under both standard and rephrased questions.
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ESC: Emotional Self-Correction for Reliable Vision-Language Models
ESC uses emotional cues triggered by an external verifier to enable training-free self-correction in VLMs, improving reliability on safety, hallucination, and reasoning benchmarks.
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Delineating Knowledge Boundaries for Honest Large Vision-Language Models
VLMs fine-tuned on a consistency-probed Visual-Idk dataset via SFT and preference optimization raise truthful rate from 57.9% to 67.3% and show internal evidence of genuine boundary recognition.