  Retrieval-augmented generation framework can address the limitations of large
language models by enabling real-time knowledge updates for more accurate
answers. An efficient way in the training phase of retrieval-augmented models
is attention distillation, which uses attention scores as a supervision signal
instead of manually annotated query-document pairs. Despite its growing
popularity, the detailed mechanisms behind the success of attention
distillation remain unexplored, particularly the specific patterns it leverages
to benefit training. In this paper, we address this gap by conducting a
comprehensive review of attention distillation workflow and identifying key
factors influencing the learning quality of retrieval-augmented language
models. We further propose indicators for optimizing models' training methods
and avoiding ineffective training.
