  Large Vision-Language Models (LVLMs) are gaining traction for their
remarkable ability to process and integrate visual and textual data. Despite
their popularity, the capacity of LVLMs to generate precise, fine-grained
textual descriptions has not been fully explored. This study addresses this gap
by focusing on \textit{distinctiveness} and \textit{fidelity}, assessing how
models like Open-Flamingo, IDEFICS, and MiniGPT-4 can distinguish between
similar objects and accurately describe visual features. We proposed the
Textual Retrieval-Augmented Classification (TRAC) framework, which, by
leveraging its generative capabilities, allows us to delve deeper into
analyzing fine-grained visual description generation. This research provides
valuable insights into the generation quality of LVLMs, enhancing the
understanding of multimodal language models. Notably, MiniGPT-4 stands out for
its better ability to generate fine-grained descriptions, outperforming the
other two models in this aspect. The code is provided at
\url{https://anonymous.4open.science/r/Explore_FGVDs-E277}.
