Answer Token

Answer token research focuses on understanding how language models identify and utilize specific words or sub-word units to construct answers in question-answering tasks. Current research investigates how different model architectures, including transformers and retrieval-augmented generation (RAG) systems, process and weigh these tokens, often employing techniques like attention mechanisms and saliency mapping to pinpoint their contribution to the final answer. This work aims to improve the accuracy, interpretability, and trustworthiness of language models by clarifying the role of answer tokens in the generation process, leading to more reliable and explainable AI systems for various applications.