Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning

TL;DR

This paper introduces an interpretable analysis of multi-hop reasoning in LLMs, revealing parallel reasoning paths and the generation of intermediate answer representations during inference.

Contribution

It presents a novel linear transformation model of reasoning processes and uncovers parallel reasoning strategies in LLMs' internal layers.

Findings

Interpretable embeddings represent potential intermediate answers.

Parallel reasoning paths are activated even without necessary knowledge.

Middle layers generate highly interpretable, compositional representations.

Abstract

Large language models (LLMs) have shown an impressive ability to perform tasks believed to require thought processes. When the model does not document an explicit thought process, it becomes difficult to understand the processes occurring within its hidden layers and to determine if these processes can be referred to as reasoning. We introduce a novel and interpretable analysis of internal multi-hop reasoning processes in LLMs. We demonstrate that the prediction process for compositional reasoning questions can be modeled using a simple linear transformation between two semantic category spaces. We show that during inference, the middle layers of the network generate highly interpretable embeddings that represent a set of potential intermediate answers for the multi-hop question. We use statistical analyses to show that a corresponding subset of tokens is activated in the model's output, implying the existence of parallel reasoning paths. These observations hold true even when the model lacks the necessary knowledge to solve the task. Our findings can help uncover the strategies that LLMs use to solve reasoning tasks, offering insights into the types of thought processes that can emerge from artificial intelligence. Finally, we also discuss the implication of cognitive modeling of these results.