  Large Vision-Language Models (LVLMs) have recently played a dominant role in
multimodal vision-language learning. Despite the great success, it lacks a
holistic evaluation of their efficacy. This paper presents a comprehensive
evaluation of publicly available large multimodal models by building a LVLM
evaluation Hub (LVLM-eHub). Our LVLM-eHub consists of $8$ representative LVLMs
such as InstructBLIP and MiniGPT-4, which are thoroughly evaluated by a
quantitative capability evaluation and an online arena platform. The former
evaluates $6$ categories of multimodal capabilities of LVLMs such as visual
question answering and embodied artificial intelligence on $47$ standard
text-related visual benchmarks, while the latter provides the user-level
evaluation of LVLMs in an open-world question-answering scenario. The study
reveals several innovative findings. First, instruction-tuned LVLM with massive
in-domain data such as InstructBLIP heavily overfits many existing tasks,
generalizing poorly in the open-world scenario. Second, instruction-tuned LVLM
with moderate instruction-following data may result in object hallucination
issues (i.e., generate objects that are inconsistent with target images in the
descriptions). It either makes the current evaluation metric such as CIDEr for
image captioning ineffective or generates wrong answers. Third, employing a
multi-turn reasoning evaluation framework can mitigate the issue of object
hallucination, shedding light on developing an effective pipeline for LVLM
evaluation. The findings provide a foundational framework for the conception
and assessment of innovative strategies aimed at enhancing zero-shot multimodal
techniques. Our LVLM-eHub will be available at
https://github.com/OpenGVLab/Multi-Modality-Arena
