Do Efficient Transformers Really Save Computation?
Kai Yang Jan Ackermann Zhenyu He Guhao Feng Bohang Zhang Yunzhen Feng Qiwei Ye Di He Liwei Wang
As transformer-based language models are trained on increasingly large
datasets and with vast numbers of parameters, finding more efficient
alternatives to the standard Transformer has become very valuable. While many
efficient Transformers and Transformer alternatives have been proposed, none
provide theoretical guarantees that they are a suitable replacement for the
standard Transformer. This makes it challenging to identify when to use a
specific model and what directions to prioritize for further investigation. In
this paper, we aim to understand the capabilities and limitations of efficient
Transformers, specifically the Sparse Transformer and the Linear Transformer.
We focus on their reasoning capability as exhibited by Chain-of-Thought (CoT)
prompts and follow previous works to model them as Dynamic Programming (DP)
problems. Our results show that while these models are expressive enough to
solve general DP tasks, contrary to expectations, they require a model size
that scales with the problem size. Nonetheless, we identify a class of DP
problems for which these models can be more efficient than the standard
Transformer. We confirm our theoretical results through experiments on
representative DP tasks, adding to the understanding of efficient Transformers'
practical strengths and weaknesses.