How does Chain of Thought decompose complex tasks?
Many language tasks can be modeled as classification problems where a large language model (LLM) is given a prompt and selects one among many possible answers. We show that the classification error in such problems scales as a power law in the number of classes. This has a dramatic consequence: the prediction error can be reduced substantially by splitting the overall task into a sequence of smaller classification problems, each with the same number of classes ("degree"). This tree-structured decomposition models chain-of-thought (CoT). It has been observed that CoT-based predictors perform better when they "think", i.e., when they develop a deeper tree, thus decomposing the problem into ...
| Authors | Amrut Nadgir, Vijay Balasubramanian, Pratik Chaudhari |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 21 May 2026 |
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