VLIS: Unimodal Language Models Guide Multimodal Language Generation

• URL: http://arxiv.org/abs/2310.09767v2
• Date: Tue, 19 Dec 2023 13:01:50 GMT
• Title: VLIS: Unimodal Language Models Guide Multimodal Language Generation
• Authors: Jiwan Chung, Youngjae Yu
• Abstract summary: We introduce Visual-Language models as Importance Sampling weights (VLIS) It combines the visual conditioning capability of vision-language models with the language understanding of unimodal text-only language models without further training. VLIS improves vision-language models on diverse tasks, including commonsense understanding and complex text generation.
• Score: 23.094728230459125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal language generation, which leverages the synergy of language and vision, is a rapidly expanding field. However, existing vision-language models face challenges in tasks that require complex linguistic understanding. To address this issue, we introduce Visual-Language models as Importance Sampling weights (VLIS), a novel framework that combines the visual conditioning capability of vision-language models with the language understanding of unimodal text-only language models without further training. It extracts pointwise mutual information of each image and text from a visual-language model and uses the value as an importance sampling weight to adjust the token likelihood from a text-only model. VLIS improves vision-language models on diverse tasks, including commonsense understanding (WHOOPS, OK-VQA, and ScienceQA) and complex text generation (Concadia, Image Paragraph Captioning, and ROCStories). Our results suggest that VLIS represents a promising new direction for multimodal language generation.

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