Rationale Alignment

Rationale alignment focuses on improving the interpretability and reliability of AI models by aligning their internal decision-making processes (rationales) with human understanding and desired outcomes. Current research emphasizes enriching training data with machine-generated or human-annotated rationales, exploring various model architectures like large language models and graph neural networks to generate and utilize these explanations, and developing new evaluation metrics to assess the quality and utility of rationales. This work is significant because improved rationale alignment enhances model transparency, trustworthiness, and ultimately, the safe and effective deployment of AI systems across diverse applications.