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Found 5 papers, 5 papers with code
MiniCPM-V: A GPT-4V Level MLLM on Your Phone
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone.
Ranked #7 on
Multiple-choice
on Neptune-Full
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.
Ranked #1 on
Visual Question Answering
on AMBER
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction.
Ranked #1 on
Visual Question Answering
on VQA v2
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
The capabilities of MLLMs depend on two crucial factors: the model architecture to facilitate the feature alignment of visual modules and large language models; the multimodal instruction tuning datasets for human instruction following.
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i. e., lack of large-scale, high-quality image-text data).
