From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models

Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Tiong, Boyang Li, Dacheng Tao, Steven HOI

Keywords: Large Language Model, Visual Question Answer, Prompts, Zero-Shot

Abstract: Large language models (LLMs) have demonstrated excellent zero-shot generalization to new tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnection and task disconnection between LLM and VQA task. End-to-end training on vision and language data may bridge the disconnections, but is inflexible and computationally expensive. To address this issue, we propose \emph{Img2Prompt}, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform VQA tasks without end-to-end training. In order to provide such prompts, we further employ LLM-agnostic models to provide prompts that can describe image content and self-constructed question-answer pairs, which can effectively guide LLM to perform VQA tasks. Img2Prompt offers the following benefits: 1) It is LLM-agnostic and can work with any LLM to perform VQA. 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. 3) It achieves comparable or better performance than methods relying on end-to-end training. On the challenging A-OKVQA dataset, our method outperforms some few-shot methods by as much as 20\%.

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