Prompting Large Vision-Language Models for Compositional Reasoning

Timothy Ossowski; Ming Jiang; Junjie Hu

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arXiv · 2401.11337

Prompting Large Vision-Language Models for Compositional Reasoning

Timothy Ossowski·Ming Jiang·Junjie Hu

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Abstract

Vision-language models such as CLIP have shown impressive capabilities in encoding texts and images into aligned embeddings, enabling the retrieval of multimodal data in a shared embedding space. However, these embedding-based models still face challenges in effectively matching images and texts with similar visio-linguistic compositionality, as evidenced by their performance on the recent Winoground dataset. In this paper, we argue that this limitation stems from two factors: the use of single vector representations for complex multimodal data, and the absence of step-by-step reasoning in these embedding-based methods. To address this issue, we make an exploratory step using a novel generative method that prompts large vision-language models (e.g., GPT-4) to depict images and perform compositional reasoning. Our method outperforms other embedding-based methods on the Winoground dataset, and obtains further improvement of up to 10% accuracy when enhanced with the optimal description.

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Timothy Ossowski, Ming Jiang, Junjie Hu. 2024-01-20. Prompting Large Vision-Language Models for Compositional Reasoning. https://arxiv.org/abs/2401.11337

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