When it comes to pre-training Multimodal LLMs (MLLMs), what are the critical factors for performance? The study titled MM1 delves into architecture components and data choices for building effective MLLMs:

• Image-caption, interleaved image-text, and text-only data are key for top few-shot results.
• The image encoder, image resolution, and image token count dramatically affect MLLMs’ capabilities.
• Despite the negligible importance of the vision-language connector design, large-scale MLLMs gain enhanced in-context learning abilities.
• MM1 showcases SOTA multimodal models, with implications for few-shot chain-of-thought prompting.

This comprehensive analysis provides a roadmap for future MLLM development, emphasizing data diversity and encoder strength. The implications for in-context learning are particularly exciting, suggesting a future where fewer data points yield stronger AI predictions. More details can be found here.