More Efficient Artificial Intelligence through MoBA: A New Approach for Long Contexts

The development of Large Language Models (LLMs) towards Artificial General Intelligence (AGI) depends significantly on extending their context length. The more information a model can process simultaneously, the more complex tasks it can handle. However, the conventional attention mechanisms that form the core of LLMs are reaching their limits, as their computational complexity increases quadratically with context length. This leads to enormous computational effort and limits the practical applicability of LLMs with very long contexts.

Previous approaches try to address this issue either through structural adjustments like Sink or Window Attention, which are often task-specific and restrict the flexibility of the models. Alternatively, linear approximations of the attention mechanism are used, but their effectiveness in complex reasoning tasks has not yet been sufficiently explored.

A new approach called "Mixture of Block Attention" (MoBA) now promises to significantly improve the efficiency of LLMs with long contexts without compromising performance. MoBA is based on the principle of "Mixture of Experts" (MoE) and applies it to the attention mechanism. Instead of weighting all parts of the input equally, as with conventional approaches, MoBA selectively focuses on the most relevant information blocks. This reduces the computational effort without impairing the model's ability to grasp complex relationships.

A key advantage of MoBA is the ability to seamlessly switch between full and sparse attention. This allows dynamic adaptation to the respective task and optimizes resource utilization. For simpler tasks, sparse attention can be used to save computing power. For more complex tasks that require a more comprehensive analysis of the context, the model can switch to full attention to achieve optimal results.

Initial implementations of MoBA show promising results and demonstrate the practical feasibility of the approach. For example, MoBA is already being used to support long-context queries in Kimi, an AI assistant. Experience shows that MoBA enables a significant improvement in the efficiency of attention calculation in LLMs and thus makes an important contribution to the further development of AI models.

Research in the field of efficient attention mechanisms for LLMs is of great importance for the future development of AI. MoBA represents a promising approach that enables the scalability of LLMs to long contexts, thus paving the way for more complex and powerful AI systems. Further research and development of MoBA and similar approaches will be crucial to fully realizing the potential of AI.

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