Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models

Javier Gonzalez, Aditya V. Nori

Keywords: Reasoning, Large Language Models, probabilities of causation

Abstract:

Recent advances in AI have been significantly driven by the capabilities of large language models (LLMs) to solve complex problems in ways that resemble human thinking. However, there is an ongoing debate about the extent to which LLMs are capable of
actual reasoning. Central to this debate are two key probabilistic concepts that are essential for connecting causes
to their effects: the probability of necessity (PN) and the probability of sufficiency (PS). This paper introduces a framework that is both theoretical and practical, aimed at assessing how effectively LLMs are able to replicate real-world reasoning mechanisms using these probabilistic measures. By viewing LLMs as abstract machines that process information through a natural language interface, we examine the conditions under which it is possible to compute suitable approximations of PN and PS. Our research marks an important step towards gaining a deeper understanding of when LLMs are capable of reasoning, as illustrated by a series of math examples.

Primary Area: Generative models

Submission Number: 9073