Attribution Evaluation

Attribution evaluation focuses on explaining the reasoning behind a model's predictions by identifying the contribution of individual features or components. Current research emphasizes developing and benchmarking methods for various model types, including graph neural networks and large language models, often using axiomatic approaches to ensure fairness and interpretability. This work is crucial for improving model transparency, trustworthiness, and ultimately, for building more reliable and robust AI systems across diverse applications like finance, image generation, and question answering. A key challenge lies in creating effective automatic evaluation methods that accurately capture nuanced relationships between model inputs and outputs, surpassing the limitations of current approaches.