Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture
Jingze Shi, Bingheng Wu
2024-12-17
Summary
This paper talks about Wonderful Matrices, a new approach to building more efficient and effective foundation models for tasks in machine learning by combining sequence transformation and state transformation techniques.
What's the problem?
Foundation models, like those used in natural language processing, often require a lot of computational resources and time to perform well. Traditional methods can be slow and may not effectively handle complex tasks due to their reliance on large amounts of data and processing power. Additionally, they can struggle with accuracy when dealing with complicated information.
What's the solution?
The authors propose Wonderful Matrices, which integrates two key methods: sequence transformation and state transformation. They introduce several innovations, including rotary position embedding to improve how the model understands the order of information, dynamic mask attention to filter out irrelevant data while maintaining high accuracy, and a mixture of experts system that speeds up the retrieval process significantly. These improvements help the model perform better while using fewer resources.
Why it matters?
This research is important because it could lead to faster and more efficient machine learning models that are easier to use and more accessible for various applications. By improving how models process information, Wonderful Matrices can help advance technology in fields like artificial intelligence, making it possible to tackle more complex tasks with less computational power.
Abstract
In order to make the foundation model more efficient and effective, our idea is combining sequence transformation and state transformation. First, we prove the availability of rotary position embedding in the state space duality algorithm, which reduces the perplexity of the hybrid quadratic causal self-attention and state space duality by more than 4%, to ensure that the combining sequence transformation unifies position encoding. Second, we propose dynamic mask attention, which maintains 100% accuracy in the more challenging multi-query associative recall task, improving by more than 150% compared to quadratic causal self-attention and state space duality, to ensure that the combining sequence transformation selectively filters relevant information. Third, we design cross domain mixture of experts, which makes the computational speed of expert retrieval with more than 1024 experts 8 to 10 times faster than the mixture of experts, to ensure that the combining state transformation quickly retrieval mixture. Finally, we summarize these matrix algorithms that can form the foundation model: Wonderful Matrices, which can be a competitor to popular model architectures.