In a groundbreaking study titled “Chain of Thought Empowers Transformers to Solve Inherently Serial Problems,” researchers Zhiyuan Li and colleagues unveil a novel approach to enhancing the capabilities of large language models (LLMs) through the use of a “chain of thought” (CoT) process. This technique instructs models to generate a sequence of intermediate steps, significantly improving their accuracy on complex arithmetic and symbolic reasoning tasks. The study delves into the theoretical underpinnings of CoT, revealing its ability to enable inherently serial computations in transformer models, which traditionally struggle with such tasks due to their parallel computation nature.
The research highlights a significant limitation of constant-depth transformers with constant-bit precision, which can only solve problems within a very restricted computational class without CoT. However, by implementing CoT, these transformers can tackle a broader range of problems, equivalent to those solvable by boolean circuits of a certain size. This enhancement is particularly notable in tasks that challenge parallel computation, such as permutation group compositions and circuit value problems, showcasing dramatic accuracy improvements, especially in models with lower depth. This study not only sheds light on the mechanics behind CoT but also opens new avenues for enhancing transformer models’ problem-solving abilities.
Read more at arXiv.org…