Synthetic Explanation

Synthetic explanation leverages artificial intelligence, particularly large language models (LLMs), to generate explanations for various tasks, aiming to improve model performance, address data scarcity, and enhance explainability. Current research focuses on applying this technique to fact verification, image analysis (e.g., detecting manipulated medical images), and information retrieval, often employing fine-tuning of pre-trained LLMs or generative models like GANs and diffusion models to create synthetic data or explanations. This approach holds significant promise for improving the reliability and trustworthiness of AI systems across diverse scientific domains and practical applications by mitigating issues like data imbalance and the high cost of human annotation.