With the rise of Large Language Models(LLMs), it has become crucial to understand their capabilities and limitations in deciphering and explaining the complex web of causal relationships that language entails. Current methods use either explicit or implicit causal reasoning, yet there is a strong need for a unified approach combining both to tackle a wide array of causal relationships more effectively. This research proposes a novel architecture called Context Aware Reasoning Enhancement with Counterfactual Analysis(CARE CA) framework to enhance causal reasoning and explainability. The proposed framework incorporates an explicit causal detection module with ConceptNet and counterfactual statements, as well as implicit causal detection through LLMs. Our framework goes one step further with a layer of counterfactual explanations to accentuate LLMs understanding of causality. The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning. The counterfactual sentences add explicit knowledge of the not caused by scenarios. By combining these powerful modules, our model aims to provide a deeper understanding of causal relationships, enabling enhanced interpretability. Evaluation of benchmark datasets shows improved performance across all metrics, such as accuracy, precision, recall, and F1 scores. We also introduce CausalNet, a new dataset accompanied by our code, to facilitate further research in this domain.

翻译：摘要：随着大型语言模型（LLMs）的兴起，理解其在解读和解释语言所蕴含的复杂因果网络方面的能力与局限性变得至关重要。当前方法采用显式或隐式因果关系推理，但亟需一种结合两者的统一方法，以更有效地处理广泛的因果关系。本研究提出一种名为"基于反事实分析的上下文感知推理增强框架"（CARE CA）的新型架构，以提升因果推理与可解释性。该框架结合了基于ConceptNet和反事实语句的显式因果检测模块，以及通过LLMs实现的隐式因果检测。我们的框架进一步引入反事实解释层，以强化LLMs对因果关系的理解。ConceptNet的知识增强了多项因果推理任务（如因果发现、因果识别和反事实推理）的性能，而反事实语句则为"非因果"场景提供了显式知识。通过整合这些强大模块，本模型旨在深化对因果关系的理解，提升可解释性。基准数据集的评估显示，所有指标（包括准确率、精确率、召回率和F1分数）均有所提升。我们还发布了CausalNet这一新数据集及配套代码，以推动该领域的进一步研究。