  Multi-modal Large Language Models (MLLMs) are increasingly prominent in the
field of artificial intelligence. Visual instruction fine-tuning (IFT) is a
vital process for aligning MLLMs' output with user's intentions. High-quality
and diversified instruction following data is the key to this fine-tuning
process. Recent studies propose to construct visual IFT datasets through a
multifaceted approach: transforming existing datasets with rule-based
templates, employing GPT-4 for rewriting annotations, and utilizing GPT-4V for
visual dataset pseudo-labeling. LLaVA-1.5 adopted similar approach and
construct LLaVA-mix-665k, which is one of the simplest, most widely used, yet
most effective IFT datasets today. Notably, when properly fine-tuned with this
dataset, MLLMs can achieve state-of-the-art performance on several benchmarks.
However, we noticed that models trained with this dataset often struggle to
follow user instructions properly in multi-round dialog. In addition, tradition
caption and VQA evaluation benchmarks, with their closed-form evaluation
structure, are not fully equipped to assess the capabilities of modern
open-ended generative MLLMs. This problem is not unique to the LLaVA-mix-665k
dataset, but may be a potential issue in all IFT datasets constructed from
image captioning or VQA sources, though the extent of this issue may vary. We
argue that datasets with diverse and high-quality detailed instruction
following annotations are essential and adequate for MLLMs IFT. In this work,
we establish a new IFT dataset, with images sourced from the COCO dataset along
with more diverse instructions. Our experiments show that when fine-tuned with
out proposed dataset, MLLMs achieve better performance on open-ended evaluation
benchmarks in both single-round and multi-round dialog setting.
