Compositional Reasoning with Transformers, RNNs, and Chain of Thought

Gilad Yehudai, Noah Amsel, Joan Bruna

Keywords: learning theory, expressiveness, transformers

TL;DR: We show that solving compositional reasoning problems requires transformers, RNNs, or CoT-augmented transformers to scale specific hyperparameters with input size, revealing distinct strengths and trade-offs across architectures.

Abstract: It is understood that different neural network architectures are suited to different tasks, but is there always a single best architecture for a given task? We compare the expressive power of transformers, RNNs, and transformers with chain of thought tokens on a simple and natural class of tasks we term Compositional Reasoning Questions (CRQ). This family captures multi-step problems with tree-like compositional structure, such as evaluating Boolean formulas. We prove that under standard hardness assumptions, *none* of these three architectures is capable of solving CRQs unless some hyperparameter (depth, embedding dimension, and number of chain of thought tokens, respectively) grows with the size of the input. We then provide constructions for solving CRQs with each architecture. For transformers, our construction uses depth that is logarithmic in the problem size. For RNNs, logarithmic embedding dimension is necessary and sufficient, so long as the inputs are provided in a certain order. For transformers with chain of thought, our construction uses $n$ CoT tokens. These results show that, while CRQs are inherently hard, there are several different ways for language models to overcome this hardness. Even for a single class of problems, each architecture has strengths and weaknesses, and none is strictly better than the others.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 7069