<ul><li>LLaDA-V is a new Multimodal Large Language Model (MLLM) that integrates visual instruction tuning with masked diffusion models, moving away from autoregressive paradigms in multimodal approaches.</li><li>LLaDA-V, built upon LLaDA, incorporates a vision encoder and MLP connector to align visual features with the language embedding space, showing competitive performance in multimodal tasks despite being weaker on textual tasks.</li><li>LLaDA-V outperforms existing hybrid autoregressive-diffusion and purely diffusion-based MLLMs in multimodal understanding, demonstrating potential for large language diffusion models in multimodal contexts.</li><li>The research findings indicate the effectiveness of LLaDA-V's architecture for multimodal tasks and suggest a need for further investigation into large language diffusion models for future research.</li></ul>

LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning

Discover more