From Corpora to Causality: Unveiling Causal Comprehension in Large Language Models

Tao Feng, Lizhen Qu, Niket Tandon, Zhuang Li, Xiaoxi Kang, Gholamreza Haffari

Keywords: language model, causality, pre-training data

TL;DR: This paper provides a comprehensive analysis of how LLMs understand causal relations.

Abstract: This study investigates the efficacy of Large Language Models (LLMs) in causal discovery. Using newly available open-source LLMs, OLMo and BLOOM, which provide access to their pre-training corpora, we explore three research questions aimed at understanding how LLMs process causal discovery. These questions focus on the impact of memorization versus generalization, the influence of incorrect causal relations in pre-training data, and the role of contexts of causal relations. Our findings indicate that while LLMs are effective in recognizing causal relations that occur frequently in pre-training data, their ability to generalize to new or rare causal relations is limited. Moreover, the presence of incorrect causal relations significantly undermines the confidence of LLMs in corresponding correct causal relations, and the context of a causal relation markedly affects the performance of LLMs to identify causal relations. This study shows that LLMs possess a limited capacity to generalize novel causal relations. It also highlights the importance of managing incorrect causal relations in pre-training data and integrating contextual information to optimize LLM performance in causal discovery tasks.

Primary Area: causal reasoning

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Submission Number: 6531