Why Depth Matters in Parallelizable Sequence Models: A Lie Algebraic View
Researchers have developed a Lie-algebraic framework to analyze the expressivity and error bounds of parallelizable sequence models like Transformers. Their theory establishes a direct link between a model's depth and its expressivity, showing that increasing depth exponentially reduces approximation error. This theoretical insight was validated through experiments on symbolic and continuous-valued state-tracking tasks, confirming the empirical performance of deep sequence models. AI
IMPACT Provides a theoretical foundation for understanding and improving the performance of deep sequence models.