Tree Based Explanation

Tree-based explanation methods aim to enhance the interpretability of complex machine learning models by representing their reasoning processes as hierarchical trees. Current research focuses on developing efficient algorithms, such as those leveraging pretrained embeddings and GFlowNet, to generate these tree structures for various model types, including large language models and boosted trees. This work addresses the need for explainable AI, particularly in high-stakes applications like healthcare and robotics, by providing human-understandable insights into model predictions and facilitating debugging. The resulting interpretable explanations improve model trustworthiness and allow for more effective model development and deployment.