Key Points
- The paper addresses the limitations of current language models in understanding aspects of the world not easily described in words, and their struggle with complex, long-form tasks.
- Video sequences can provide valuable temporal information absent in language and static images, making them attractive for joint modeling with language to develop a broader understanding of the world.
- Challenges in learning from millions of tokens of video and language sequences were addressed through the curation of a large dataset of diverse videos and books.
- The paper presents the development of one of the largest context size transformers to date for training on long video and language sequences, setting new benchmarks in difficult retrieval tasks and long video understanding.
- Solutions for overcoming vision-language training challenges were proposed, including using masked sequence packing, loss weighting, and model-generated QA dataset for long sequence chat.
- The implementation utilized RingAttention, masked sequence packing, and other key features for training on millions-length multimodal sequences.
- A family of 7B parameter models capable of processing long text documents and videos of over 1M tokens was fully open-sourced.
- The paper demonstrated the capability of the model in answering questions about a 1-hour YouTube compilation of over 500 video clips and achieving competitive performance in needle retrieval tasks.
- The paper also discussed limitations and future work, including the need for better video tokenization, incorporation of more modalities, and improvement of video datasets.
Summary
The paper discusses the limitations of current language models in understanding complex, long-form tasks and the potential of joint modeling with video sequences to overcome these limitations. The paper outlines the challenges of learning from large datasets of video and language sequences and presents solutions to address these challenges, including utilizing a large dataset of diverse videos and books, implementing the RingAttention technique, and gradually increasing context size. The paper introduces a highly optimized implementation with key features for training on multimodal sequences and shares the contributions of the work, including training one of the largest context size transformers and fully open-sourcing a family of 7B parameter models capable of processing long text documents and videos. The paper also presents training stages and datasets, as well as the model's architecture and inference scalability. It discusses the related work in the field and addresses limitations and future work.
Contributions and Implications
Overall, the paper presents a comprehensive approach to improving language models' understanding of the world by combining language and video and addresses challenges in training on massive datasets of long video and language sequences. The use of RingAttention, masked sequence packing, and other key features for training on millions-length sequences demonstrates promising results in understanding over 1-hour-long videos and long-form language sequences. The paper's contributions are expected to pave the way for advancing AI models with reliable reasoning and a grounded understanding of the world and broader capabilities.
Reference: https://arxiv.org/abs/2402.08268