Large language models (LLMs) have shown various ability on natural language processing, including problems about causality. It is not intuitive for LLMs to command causality, since pretrained models usually work on statistical associations, and do not focus on causes and effects in sentences. So that probing internal manipulation of causality is necessary for LLMs. This paper proposes a novel approach to probe causality manipulation hierarchically, by providing different shortcuts to models and observe behaviors. We exploit retrieval augmented generation (RAG) and in-context learning (ICL) for models on a designed causality classification task. We conduct experiments on mainstream LLMs, including GPT-4 and some smaller and domain-specific models. Our results suggest that LLMs can detect entities related to causality and recognize direct causal relationships. However, LLMs lack specialized cognition for causality, merely treating them as part of the global semantic of the sentence.

本研究解决了大型语言模型在因果关系方面的能力不足问题，提出了一种分层探究因果关系操控的新方法。通过使用检索增强生成和上下文学习，我们的实验显示，尽管大型语言模型能够识别与因果关系相关的实体，直接的因果关系依然未能被它们深刻理解。

探究大型语言模型的因果关系操控