Synergizing Causal Inference and LLMs

The collaboration between LLMs and Causal Inference is scrutinized in a comprehensive survey, shedding light on the reciprocal benefits and potential advancements in AI.

• Causal inference leads to breakthroughs in LLMs by capturing nuanced relationships among variables.
• Enhanced features include increased predictive accuracy, fairness, explainability, and robustness.
• In turn, LLMs’ reasoning abilities help in identifying causal relationships and estimating effects.

The fusion of LLMs and causal frameworks is spawning an AI evolution geared towards more fair, robust, and interpretable models. It accentuates the necessity of understanding underlying causes and not merely data correlations, aspiring for AI with a deeper comprehension of the world.