  Vision-extended LLMs have made significant strides in Visual Question
Answering (VQA). Despite these advancements, VLLMs still encounter substantial
difficulties in handling queries involving long-tail entities, with a tendency
to produce erroneous or hallucinated responses. In this work, we introduce a
novel evaluative benchmark named \textbf{SnapNTell}, specifically tailored for
entity-centric VQA. This task aims to test the models' capabilities in
identifying entities and providing detailed, entity-specific knowledge. We have
developed the \textbf{SnapNTell Dataset}, distinct from traditional VQA
datasets: (1) It encompasses a wide range of categorized entities, each
represented by images and explicitly named in the answers; (2) It features QA
pairs that require extensive knowledge for accurate responses. The dataset is
organized into 22 major categories, containing 7,568 unique entities in total.
For each entity, we curated 10 illustrative images and crafted 10
knowledge-intensive QA pairs. To address this novel task, we devised a
scalable, efficient, and transparent retrieval-augmented multimodal LLM. Our
approach markedly outperforms existing methods on the SnapNTell dataset,
achieving a 66.5\% improvement in the BELURT score. We will soon make the
dataset and the source code publicly accessible.
