Contextual Causality with Large Language Models: A Survey

2026-09-18

AI Summary

本文通过提出上下文因果关系的分类、分析现有研究局限及未来方向，系统探索了大型语言模型在理解上下文因果关系方面的问题。

Abstract

Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more reliable decision-making. Despite its significance, a systematic exploration of contextual causality with LLMs is still lacking. To fill this gap, we present a comprehensive survey on this topic. In this survey, we first propose a taxonomy of contextual causality, consisting of semantic, intervention, and counterfactual causality, and characterize each category by its core causal question, required model capabilities, representative tasks, and practical uses in causality analysis. We then analyze existing studies and discuss their key limitations. Finally, we examine the gaps between current benchmarks and real-world needs and outline promising directions for future research. Our goal is to clarify the research landscape of contextual causality with LLMs, emphasize its importance, and highlight promising future directions.

Problem

• contextual causality
• large language models
• causal relations

Innovation

• contextual causality
• large language models
• taxonomy of causal relations
• semantic, intervention, and counterfactual causality