  Vision language models (VLMs) have achieved impressive progress in diverse
applications, becoming a prevalent research direction. In this paper, we build
FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn
conversations that are derived from 27 source datasets, empowering VLMs to
spontaneously refine their responses based on user feedback across diverse
tasks. To scale up the data collection, FIRE is collected in two components:
FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is
freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a
benchmark to comprehensively evaluate the feedback-refining capability of VLMs,
which contains 11K feedback-refinement conversations as the test data, two
evaluation settings, and a model to provide feedback for VLMs. We develop the
FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows
remarkable feedback-refining capability on FIRE-Bench and outperforms untrained
VLMs by 50%, making more efficient user-agent interactions and underscoring the
significance of the FIRE dataset.
