  Multi-modal open-domain question answering typically requires evidence
retrieval from databases across diverse modalities, such as images, tables,
passages, etc. Even Large Language Models (LLMs) like GPT-4 fall short in this
task. To enable LLMs to tackle the task in a zero-shot manner, we introduce
MoqaGPT, a straightforward and flexible framework. Using a divide-and-conquer
strategy that bypasses intricate multi-modality ranking, our framework can
accommodate new modalities and seamlessly transition to new models for the
task. Built upon LLMs, MoqaGPT retrieves and extracts answers from each
modality separately, then fuses this multi-modal information using LLMs to
produce a final answer. Our methodology boosts performance on the MMCoQA
dataset, improving F1 by +37.91 points and EM by +34.07 points over the
supervised baseline. On the MultiModalQA dataset, MoqaGPT surpasses the
zero-shot baseline, improving F1 by 9.5 points and EM by 10.1 points, and
significantly closes the gap with supervised methods. Our codebase is available
at https://github.com/lezhang7/MOQAGPT.
