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Found 2 papers, 1 papers with code
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions.
Hallucination Augmented Contrastive Learning for Multimodal Large Language Model
We first analyzed the representation distribution of textual and visual tokens in MLLM, revealing two important findings: 1) there is a significant gap between textual and visual representations, indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled, making it challenging to distinguish them.
Ranked #78 on
Visual Question Answering
on MM-Vet
