Filling the Image Information Gap for VQA: Prompting Large Language
  Models to Proactively Ask Questions
Ziyue Wang Chi Chen Peng Li Yang Liu
Large Language Models (LLMs) demonstrate impressive reasoning ability and the
maintenance of world knowledge not only in natural language tasks, but also in
some vision-language tasks such as open-domain knowledge-based visual question
answering (OK-VQA). As images are invisible to LLMs, researchers convert images
to text to engage LLMs into the visual question reasoning procedure. This leads
to discrepancies between images and their textual representations presented to
LLMs, which consequently impedes final reasoning performance. To fill the
information gap and better leverage the reasoning capability, we design a
framework that enables LLMs to proactively ask relevant questions to unveil
more details in the image, along with filters for refining the generated
information. We validate our idea on OK-VQA and A-OKVQA. Our method
continuously boosts the performance of baselines methods by an average gain of
2.15% on OK-VQA, and achieves consistent improvements across different LLMs.