This paper presents VisLingInstruct, a novel approach to advancing
Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show
impressive zero-shot abilities in multi-modal tasks, but their performance
depends heavily on the quality of instructions. VisLingInstruct tackles this by
autonomously evaluating and optimizing instructional texts through In-Context
Learning, improving the synergy between visual perception and linguistic
expression in MMLMs. Alongside this instructional advancement, we have also
optimized the visual feature extraction modules in MMLMs, further augmenting
their responsiveness to textual cues. Our comprehensive experiments on MMLMs,
based on FlanT5 and Vicuna, show that VisLingInstruct significantly improves
zero-shot performance in visual multi-modal tasks. Notably, it achieves a 13.1%
and 9% increase in accuracy over the prior state-of-the-art on the TextVQA and
HatefulMemes datasets.

VisLingInstruct 通过自主评估和优化教学文本的过程，改善了多模态语言模型中视觉感知和语言表达的协同作用，这在零样本学习中显著提高了多模态任务的性能。