Attention-guided attribution is a novel, training-free methodology designed to enhance the transparency and trustworthiness of generative AI systems, particularly in summarization tasks. It operates by directly utilizing the attention weights within a model's decoder during the generation process to pinpoint the exact source segments—be it text spans or image regions—that inform each generated output token. This approach overcomes the limitations of traditional post-hoc attribution methods, which analyze model outputs after generation, or retraining-based methods, which require significant computational resources. By providing real-time, fine-grained provenance, attention-guided attribution addresses the critical need for users, especially in sensitive domains like clinical summarization, to understand the evidential basis of generated information. It is particularly relevant for researchers and ML engineers developing interpretable and deployable AI systems where accountability and verifiability are paramount.
Attention-guided attribution is a method that helps AI systems explain themselves by showing exactly which parts of the original information (like text or images) were used to create each piece of a generated summary. It does this in real-time without extra training, making AI outputs more trustworthy and easier to verify, especially in critical fields like medicine.
Attention-based attribution, Decoder attention attribution, Real-time attribution, Generation-time attribution, Source attribution
Was this definition helpful?