Transformer Layers as Painters
Qi Sun, Marc Pickett, Aakash Kumar Nain, Llion Jones
2024-07-15
Summary
This paper discusses how transformer layers in large language models (LLMs) can be understood better by comparing them to painters working on a canvas, exploring how these layers can be modified without losing their effectiveness.
What's the problem?
Despite the widespread use of transformers in AI, we don't fully understand how each layer contributes to the model's performance. When updating or modifying these models, it can be challenging to know which layers are essential and how changes might affect the overall function. This lack of understanding can lead to inefficiencies and confusion when trying to improve or adapt these models.
What's the solution?
The researchers propose an analogy where each layer of a transformer is like a painter adding details to a canvas. They conducted experiments to see how removing or reordering layers affects performance. They found that while the first and last layers have distinct functions, the middle layers are more uniform and can often be skipped or reordered without major issues. This means that the model can still work well even if some middle layers are removed or run in a different order, which allows for more flexibility in how the model is used and improved.
Why it matters?
This research is important because it provides insights into how transformer models operate, which can help developers make better decisions when updating or optimizing these AI systems. By understanding that some layers can be adjusted or skipped without losing effectiveness, we can create more efficient models that save time and resources while maintaining high performance.
Abstract
Despite their nearly universal adoption for large language models, the internal workings of transformers are not well understood. We aim to better understand the impact of removing or reorganizing information throughout the layers of a pretrained transformer. Such an understanding could both yield better usage of existing models as well as to make architectural improvements to produce new variants. We present a series of empirical studies on frozen models that show that the lower and final layers of pretrained transformers differ from middle layers, but that middle layers have a surprising amount of uniformity. We further show that some classes of problems have robustness to skipping layers, running the layers in an order different from how they were trained, or running the layers in parallel. Our observations suggest that even frozen pretrained models may gracefully trade accuracy for latency by skipping layers or running layers in parallel.