Explanation Generation

Explanation Generation is an important branch of natural language processing aimed at enhancing the transparency and interpretability of machine learning models by generating clear and accurate explanations. Its core objective is to make the model's decision-making process visible to users, helping them understand why the model makes specific predictions or recommendations. This technology has significant application value in fields such as medical diagnosis, financial analysis, and legal consultation, where it can increase user trust in the model and facilitate more effective decision support.