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The race to continually develop ever larger and deeper foundational models isunderway. However, techniques like the Chain-of-Thought (CoT) method continueto play a pivotal role in achieving optimal downstream performance. In thiswork, we establish an approximate parallel between using chain-of-thought andemploying a deeper transformer. Building on this insight, we introduceCoTFormer, a transformer variant that employs an implicit CoT-like mechanism toachieve capacity comparable to a deeper model. Our empirical findingsdemonstrate the effectiveness of CoTFormers, as they significantly outperformlarger standard transformers.

CoTFormer: More Tokens With Attention Make Up For Less Depth