Native MoE Multimodal LLM Will Be The New AI Frontier

Current State of Multimodal AI 00:00

• Many popular multimodal AI models like Claude, Grock, and Llama are not genuinely multimodal; they use late fusion techniques that treat text and images separately during training.
• Models such as Gemini and GPT-4 may be exceptions, but the late fusion approach connects separately trained components rather than integrating them from the start.
• This method allows leveraging pre-trained components that are easier to train due to existing datasets.

Early Fusion vs. Late Fusion 01:17

• Meta's research group, Metafare, has shown promise with early fusion models, which process text and visual data together from the beginning using a unified transformer architecture.
• Early fusion treats images as discrete tokens, enabling seamless interaction between text and images.
• Challenges in training early fusion models arise from competing signals between modalities, which can lead to instability.

Advantages of Early Fusion 04:48

• Chameleon, an early fusion model, can interleave documents with images and generate outputs that late fusion models cannot.
• Early fusion models have demonstrated superior performance on language and image-to-text benchmarks compared to late fusion models.
• Recent research indicates that early fusion models are more efficient, training faster, using less memory, and requiring fewer parameters.

Findings from Recent Research 06:36

• A study involving 457 models found that early fusion matches late fusion performance while being more efficient.
• Early fusion models can learn to specialize in either text or image processing spontaneously without human guidance.
• Increasing image resolution enhances early fusion performance, contradicting the belief that specialized encoders are needed for high-resolution images.

Future of Multimodal Models 08:03

• While early fusion is generally more effective, late fusion still has advantages in specific tasks, such as pure image captioning.
• The choice of model architecture should depend on the specific use case and available data.
• There may be a shift towards open-source native multimodal models, with late fusion becoming less common for general use cases.

Final Thoughts 09:31

• The development of unified multimodal models may lead to better cultural and linguistic inclusiveness in AI.
• Concerns exist about tokenizing images as continuous data, suggesting future exploration into multimodal diffusion models.
• The advancements by Meta indicate that they are leading the field, with other companies potentially lagging behind.