Self Rationalization

Self-rationalization in large language models (LLMs) focuses on enabling these models to not only produce answers but also generate explanations for their reasoning process. Current research emphasizes improving the reliability, traceability, and faithfulness of these generated explanations, often employing techniques like self-reasoning frameworks, multi-reward training, and label-adaptive learning within various model architectures. This area is significant because trustworthy explanations are crucial for building reliable AI systems and fostering user trust, particularly in high-stakes applications like fact-checking and decision support. The development of robust self-rationalization methods is thus a key challenge in advancing the trustworthiness and explainability of LLMs.