Is Knowledge All Large Language Models Needed for Causal Reasoning?
Hengrui Cai Shengjie Liu Rui Song
This paper explores the causal reasoning of large language models (LLMs) to
enhance their interpretability and reliability in advancing artificial
intelligence. Despite the proficiency of LLMs in a range of tasks, their
potential for understanding causality requires further exploration. We propose
a novel causal attribution model that utilizes "do-operators" for constructing
counterfactual scenarios, allowing us to systematically quantify the influence
of input numerical data and LLMs' pre-existing knowledge on their causal
reasoning processes. Our newly developed experimental setup assesses LLMs'
reliance on contextual information and inherent knowledge across various
domains. Our evaluation reveals that LLMs' causal reasoning ability depends on
the context and domain-specific knowledge provided, and supports the argument
that "knowledge is, indeed, what LLMs principally require for sound causal
reasoning". On the contrary, in the absence of knowledge, LLMs still maintain a
degree of causal reasoning using the available numerical data, albeit with
limitations in the calculations.