The team of LeCun and Xie Senin released the impressive multi-modal large-scale language model Cambrian-1, which is an innovative work that puts vision first. It is not only a technological breakthrough, but also represents a new thinking on multi-modal learning research. Its open source nature provides valuable resources for researchers and developers. The design of Cambrian-1 revolves around five core elements: visual representation learning, connector design, instruction fine-tuning data, instruction fine-tuning strategy and benchmark testing. It performs well on visual language tasks and is even comparable to some top proprietary models. However, the research team also frankly pointed out the model's shortcomings in dialogue capabilities and actively responded by improving training methods.
In the world of AI, we have just welcomed an eye-catching new member - Cambrian-1, a multi-modal large language model (MLLM) jointly created by industry giants such as LeCun and Xie Saining. The emergence of this model is not only a leap in technology, but also a profound reflection on multi-modal learning research.
Cambrian-1’s design philosophy puts vision first, which is especially valuable in today’s language-centric AI research. It reminds us that language is not the only way for humans to acquire knowledge, and sensory experiences such as vision, hearing, and touch are equally important. The open source of Cambrian-1 provides a valuable resource for all researchers and developers interested in multimodal learning.
The construction of this model revolves around five core elements: visual representation learning, connector design, instruction fine-tuning data, instruction fine-tuning strategy and benchmark testing. Each element is an in-depth exploration of the MLLM design space and reflects the research team's unique insights into existing problems.
It is worth mentioning that Cambrian-1’s performance on visual language tasks is impressive. Not only does it outperform other open source models, it even matches the industry's top proprietary models on some benchmarks. Behind this achievement is the research team’s innovative thinking on instruction fine-tuning and connector design.
However, Cambrian-1's research path has not been smooth sailing. The researchers found that even well-trained MLLMs may have deficiencies in conversational abilities, a phenomenon known as the "answering machine phenomenon." To solve this problem, they added system prompts to the training to encourage the model to engage in richer conversations.
The success of Cambrian-1 is inseparable from the strong research team behind it. Among them, Shengbang Tong is one of the authors of the paper, and his contribution cannot be ignored. Currently, he is studying for a PhD at New York University, under the supervision of Professor Yann LeCun and Professor Xie Saining. His research interests cover world models, unsupervised/self-supervised learning, generative models, and multimodal models.
The open source of Cambrian-1 brings a breath of fresh air to the AI community. It not only provides a powerful multi-modal learning tool, but also inspires people to think deeply about multi-modal learning research. As more and more researchers and developers join the exploration of Cambrian-1, we have reason to believe that it will become an important force in promoting the development of AI technology.
Project address: https://github.com/cambrian-mllm/cambrian
Paper: https://arxiv.org/abs/2406.16860
The emergence of Cambrian-1 has brought new possibilities to the field of multi-modal AI, and its open source nature also encourages wider cooperation and innovation. We look forward to Cambrian-1 being able to demonstrate its powerful capabilities in more fields in the future and promote the continued advancement of AI technology.