Presents Med-HallMark benchmark, MediHall Score metric, and MediHallDetector model for hallucination detection and evaluation in medical LVLMs.
Unified hallucination detection for multimodal large language models
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MoRE enables MLLMs to dynamically coordinate heterogeneous retrieval experts via Step-GRPO training, yielding over 7% average gains on open-domain QA benchmarks.
CPIL is a contrastive two-stage method that enforces paraphrase invariance on limited labeled data to outperform baselines in hallucination detection across 11 tasks.
The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.
citing papers explorer
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Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
Presents Med-HallMark benchmark, MediHall Score metric, and MediHallDetector model for hallucination detection and evaluation in medical LVLMs.
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Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation
MoRE enables MLLMs to dynamically coordinate heterogeneous retrieval experts via Step-GRPO training, yielding over 7% average gains on open-domain QA benchmarks.
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Cross Paraphrastic Invariance Learning for Hallucination Detection
CPIL is a contrastive two-stage method that enforces paraphrase invariance on limited labeled data to outperform baselines in hallucination detection across 11 tasks.
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Hallucination of Multimodal Large Language Models: A Survey
The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.