B Li, Y Ge, Y Ge, G Wang, R Wang… - Proceedings of the …, 2024 - openaccess.thecvf.com
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 …
T Yu, H Zhang, Y Yao, Y Dang, D Chen, X Lu… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
C Jiang, H Xu, M Dong, J Chen, W Ye… - Proceedings of the …, 2024 - openaccess.thecvf.com
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 …
Q Ye, H Xu, J Ye, M Yan, A Hu, H Liu… - Proceedings of the …, 2024 - openaccess.thecvf.com
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 …