Rationale Generation

Rationale generation focuses on creating explainable AI by generating textual justifications for model predictions, aiming to improve transparency, trustworthiness, and robustness. Current research emphasizes using large language models (LLMs) within various architectures, often incorporating techniques like chain-of-thought prompting, preference optimization, and contrastive learning to enhance rationale quality and alignment with human reasoning. This field is significant for advancing explainable AI, impacting areas like automated assessment, multimodal question answering, and improving the safety and reliability of AI systems in high-stakes applications.