Purpose of the paper: This paper presents a comprehensive survey of the applications and interactions between causal inference and Large Language Models (LLMs) in Natural Language Processing. The focus is to understand and improve the reasoning capabilities, fairness, and safety of LLMs, along with providing explanations and managing multimodality.

Key Insights:

• Causal inference can significantly enhance the predictive power and fairness of LLMs by revealing causal relationships.
• LLMs contribute to the field of causal inference, aiding in discovering causal connections and estimating effects.
• The collaboration between causal frameworks and LLMs holds promise for advancing AI.

Opinion: The integration of causal inference into LLMs is crucial for developing more reliable and interpretable AI systems. This research guides how causal techniques can complement and refine the capabilities of LLMs for future applications like personalized AI and ethical decision-making. Read More