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

Le Zhang, Yihong Wu, Fengran Mo, Jian-Yun Nie, Aishwarya Agrawal


Abstract
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.
Anthology ID:
2023.findings-emnlp.85
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1195–1210
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.85
DOI:
10.18653/v1/2023.findings-emnlp.85
Bibkey:
Cite (ACL):
Le Zhang, Yihong Wu, Fengran Mo, Jian-Yun Nie, and Aishwarya Agrawal. 2023. MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1195–1210, Singapore. Association for Computational Linguistics.
Cite (Informal):
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model (Zhang et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.findings-emnlp.85.pdf