Abstract Multimodal Large Language Models (MLLMs) have recently demonstrated
impressive capabilities in multimodal understanding reasoning and interaction. However …

Multimodal large language models (MLLMs) building upon the foundation of powerful large
language models (LLMs) have recently demonstrated exceptional capabilities in generating …

Hallucination posed as a pervasive challenge of multi-modal large language models
(MLLMs) has significantly impeded their real-world usage that demands precise judgment …

Learning from feedback reduces the hallucination of multimodal large language models
(MLLMs) by aligning them with human preferences. While traditional methods rely on labor …

Large Multimodal Models (LMM) are built across modalities and the misalignment between
two modalities can result in" hallucination", generating textual outputs that are not grounded …

We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint
mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision …

Multi-modal large language models (MLLMs) have been shown to efficiently integrate
natural language with visual information to handle multi-modal tasks. However MLLMs still …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated impressive
instruction abilities across various open-ended tasks. However previous methods have …

Despite the promising progress in multi-modal tasks, current large multi-modal models
(LMM) are prone to hallucinating inconsistent descriptions with respect to the associated …

Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a
variety of visual-language tasks. However, current MLLM benchmarks are predominantly …