MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training (AI summary)

Key Points

1. The paper focuses on building performant Multimodal Large Language Models (MLLMs) by studying the importance of various architecture components and data choices.

2. The authors demonstrate that large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art few-shot results across multiple benchmarks, compared to other published pre-training results.

3. The study reveals that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance.

4. The authors then scale up the model to create MM1, a family of multimodal models up to 30B parameters, which are state-of-the-art in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks.

5. Existing MLLMs fall into two categories: closed models and open models. Closed models might be available for use, but little to nothing is known about the data, model architecture, and training details. Open models release details about the data, model, and training configurations, allowing the community to build upon.

6. The paper discusses the need to distill principles and lessons of how to build such models that might outlive specific component implementations, so it proposes documenting the MLLM building process and formulating design lessons for the community.

7. The study provides insights into good choices for architecture, data, and training procedure, and it evaluates model performance through efficient experimental setups for ablations, resulting in recommended configurations for model scaling and pre-training procedure.

8. The paper reveals lessons learned from pre-training that transfer to supervised fine-tuning, such as the importance of different types of pre-training data and the negligible impact of different vision-language connector architectures on final results.

9. The authors hope that the identified lessons will help the community in building strong models beyond the any single specific model architecture or data strategy.

Summary

In the research paper, the authors delve into the construction of high-performing Multimodal Large Language Models (MLLMs). They conduct comprehensive ablations of the image encoder, vision language connector, and various pre-training data selections to identify crucial design lessons. One key finding is the significance of utilizing a blend of image-caption, interleaved image-text, and text-only data for large-scale multimodal pre-training. They also demonstrate that the image encoder, image resolution, and image token count have a substantial impact on the effectiveness of the models. The authors develop MM1, a family of multimodal models with up to 30B parameters, which achieves state-of-the-art pre-training metrics and competitive performance on various multimodal benchmarks following supervised fine-tuning. The authors emphasize that large-scale pre-training allows for enhanced in-context learning, multi-image reasoning, and few-shot chain-of-thought prompting.

The paper also explores the emergence of Multimodal Large Language Models (MLLMs) as a new frontier in foundation models, marrying language and image understanding into a single model. The authors point out that understanding how to build such models is essential for the research community, emphasizing the need to distill principles and lessons that may outlive specific implementations. The paper provides detailed insights into architectural decisions, pre-training data choices, model scaling, and the impact of image resolution and pre-training on supervised fine-tuning. The analysis includes ablations of various factors such as model architecture decisions, pre-training data choices, and the impact of image resolution and positional embedding interpolation on model performance in supervised fine-tuning. The authors also compare the performance of their models with the state-of-the-art, demonstrating the superior performance of MM1 in achieving competitive results on a wide range of benchmarks.

In summary, the paper provides detailed insights into the construction of high-performing Multimodal Large Language Models, offering valuable lessons and principles for the research community. The authors emphasize the importance of using a careful mix of image-caption, interleaved image-text, and text-only data for large-scale multimodal pre-training, as well as the impact of image resolution, image encoder design, and pre-training data choices on model performance. They also present MM1, a family of multimodal models with up to 30B parameters, which achieves state-of-the-art pre-training metrics and competitive performance on various multimodal benchmarks following supervised fine-tuning.

Reference: https://arxiv.org/abs/2403.096...