Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables. The emergence of generative Large Language Models (LLMs) has significantly impacted various NLP domains, particularly through their advanced reasoning capabilities. This survey focuses on evaluating and improving LLMs from a causal view in the following areas: understanding and improving the LLMs' reasoning capacity, addressing fairness and safety issues in LLMs, complementing LLMs with explanations, and handling multimodality. Meanwhile, LLMs' strong reasoning capacities can in turn contribute to the field of causal inference by aiding causal relationship discovery and causal effect estimations. This review explores the interplay between causal inference frameworks and LLMs from both perspectives, emphasizing their collective potential to further the development of more advanced and equitable artificial intelligence systems.

翻译：因果推断通过捕捉变量间的因果关系，在提升自然语言处理（NLP）模型的预测准确性、公平性、鲁棒性和可解释性方面展现出潜力。生成式大语言模型（LLMs）的出现，尤其是其先进的推理能力，对NLP各领域产生了深远影响。本综述从因果视角聚焦于以下方面评估和改进LLMs：理解并提升LLMs的推理能力、解决LLMs中的公平性与安全性问题、以解释性补充LLMs以及处理多模态任务。同时，LLMs强大的推理能力也能反向促进因果推断领域的发展，助力因果关系发现与因果效应估计。本文从双向视角探讨因果推断框架与LLMs之间的相互作用，强调两者协同在推动更先进、更公平的人工智能系统发展中的潜力。