CLEAR: Can Language Models Really Understand Causal Graphs?

TL;DR

This paper investigates whether large language models can understand causal graphs by developing a new benchmark and framework, revealing that models show some understanding but still have significant room for improvement.

Contribution

The paper introduces CLEAR, a novel benchmark with a framework and diverse tasks to evaluate language models' understanding of causal graphs.

Findings

Language models show preliminary understanding of causal graphs.

Models perform better on simpler tasks, struggle with complex ones.

Significant potential for improvement in models' causal reasoning abilities.

Abstract

Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive advancements in language models, a crucial question arises: can they really understand causal graphs? To this end, we pioneer an investigation into language models' understanding of causal graphs. Specifically, we develop a framework to define causal graph understanding, by assessing language models' behaviors through four practical criteria derived from diverse disciplines (e.g., philosophy and psychology). We then develop CLEAR, a novel benchmark that defines three complexity levels and encompasses 20 causal graph-based tasks across these levels. Finally, based on our framework and benchmark, we conduct extensive experiments on six leading language models and summarize five empirical findings. Our results indicate that while language models demonstrate a preliminary understanding of causal graphs, significant potential for improvement remains. Our project website is at https://github.com/OpenCausaLab/CLEAR.