Human Explanation

Human explanation in AI focuses on improving model interpretability and performance by integrating human-provided explanations into the training and inference processes. Current research heavily utilizes large language models (LLMs) like GPT-3 and others, exploring methods to leverage these models for generating, interpreting, and incorporating explanations into various tasks, including robot control, toxicity moderation, and mental health applications. This research aims to bridge the gap between complex model outputs and human understanding, ultimately leading to more trustworthy, robust, and effective AI systems across diverse domains.