Attribution Analysis

Attribution analysis seeks to explain the internal workings of complex models, particularly in AI, by identifying which inputs or features most significantly influence the model's output. Current research focuses on developing and improving attribution methods for various model types, including large language models (LLMs), vision-language models (VLLMs), and deep neural networks, often employing techniques like contrastive learning, Bayesian approaches, and multi-modal feature fusion. These advancements enhance model interpretability, facilitating better understanding of model decisions, improving model reliability, and enabling applications such as detecting AI-generated content, analyzing climate change impacts, and improving healthcare diagnostics. The ultimate goal is to move beyond "black box" models towards more transparent and trustworthy AI systems.