Language models can learn implicit multi-hop reasoning, but only if they have lots of training data

Abstract

Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought.We investigate this capability using GPT2-style language models trained from scratch on controlled k -hop reasoning datasets ( k = 2, 3, 4 ). We show that while such models can indeed learn implicit k -hop reasoning,the required training data grows exponentially in k , and the requirednumber of transformer layers grows linearly in k .We offer a theoretical explanation for why this depth growth is necessary.We further find that the data requirement can be mitigated, but not eliminated,through curriculum learning.