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

MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model