KV Shifting Attention Enhances Language Modeling
Mingyu Xu, Wei Cheng, Bingning Wang, Weipeng Chen
2024-12-06
Summary
This paper talks about KV shifting attention, a new technique designed to improve how large language models (LLMs) learn and understand language by making their attention mechanisms more efficient.
What's the problem?
Large language models are great at understanding context and learning from examples, but they often require complex structures that can slow them down and make them harder to train. These models typically rely on a mechanism called induction heads, which needs multiple layers of attention to function well. This complexity can hinder performance and efficiency.
What's the solution?
The authors propose a new method called KV shifting attention, which simplifies the induction heads mechanism by separating the keys and values in the attention process. This allows the model to perform well with fewer layers, making it faster and more efficient. They tested this new approach and found that it helps the model learn better and achieve good results more quickly, even in models with over 10 billion parameters.
Why it matters?
This research is important because it enhances the performance of language models, making them faster and easier to train. By improving how these models work, KV shifting attention can lead to better applications in natural language processing, such as chatbots, translation services, and more. This advancement could help make AI systems smarter and more effective in understanding human language.
Abstract
The current large language models are mainly based on decode-only structure transformers, which have great in-context learning (ICL) capabilities. It is generally believed that the important foundation of its ICL capability is the induction heads mechanism, which requires at least two layers attention. In order to more efficiently implement the ability of the model's induction, we revisit the induction heads mechanism and proposed a KV shifting attention. We theoretically prove that the KV shifting attention reducing the model's requirements for the depth and width of the induction heads mechanism. Our experimental results demonstrate that KV shifting attention is beneficial to learning induction heads and language modeling, which lead to better performance or faster convergence from toy models to the pre-training models with more than 10 B parameters.