Learning by Self-Explaining (LSX): Advancing AI Learning and Performance
Overview
Explainable AI (XAI) focuses on providing interpretable insights into machine learning model decisions. LSX integrates self-explanations into AI model learning, enhancing generalization and explanation faithfulness.
Key Components of LSX
LSX consists of a learner model, which performs tasks and generates explanations, and an internal critic, which evaluates explanation quality. LSX operates through an Explain, Reflect, Revise cycle, enabling continuous model improvement.
Practical Solutions and Value
LSX introduces a novel approach integrating self-explanations into AI model learning, enhancing model performance and explanation relevance. It enables models to learn from data and their own explanations, fostering deeper understanding and continuous improvement.
Experimental Evaluations
LSX demonstrates significant improvements in model generalization across various datasets. It achieves competitive or superior performance compared to traditional methods and produces relevant and accurate explanations.
Future Research Directions
Future research includes applying LSX to different modalities and tasks, integrating memory buffers for explanation refinement, and developing inherently interpretable models.
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