Free Text Explanation

Free-text explanation research focuses on generating human-understandable natural language justifications for AI model predictions, aiming to improve transparency and trustworthiness. Current efforts concentrate on efficient data generation methods, often leveraging large language models (LLMs) within encoder-decoder architectures or through prompting techniques, and developing robust evaluation metrics that capture both accuracy and faithfulness of explanations. This work is crucial for building more reliable and explainable AI systems across various applications, from question answering and grammatical error correction to toxicity detection and fact verification, ultimately fostering greater user trust and facilitating more effective human-AI collaboration.