Evaluating Interventional Reasoning Capabilities of Large Language Models

arXiv:2404.05545v1 Announce Type: new
Abstract: Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consider using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses …

abstract arxiv automate capabilities causal commonsense cs.ai cs.cl cs.lg decision decisions effects facts language language models large language large language models line llms making reasoning stat.me studying tasks type work