Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models

• URL: http://arxiv.org/abs/2406.15359v1
• Date: Fri, 29 Mar 2024 10:53:07 GMT
• Title: Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
• Authors: Jesse Atuhurra, Iqra Ali, Tatsuya Hiraoka, Hidetaka Kamigaito, Tomoya Iwakura, Taro Watanabe,
• Abstract summary: We introduce nine vision-and-language (VL) tasks and construct multilingual visual-text datasets in four languages: English, Japanese, Swahili, and Urdu. Our work is the first to conduct such analyses in Swahili and Urdu. Also, it introduces textitrationales in VL analysis, which played a vital role in the evaluation.
• Score: 25.088717058818528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have increased interest in vision language models (VLMs), which process image-text pairs as input. Studies investigating the visual understanding ability of VLMs have been proposed, but such studies are still preliminary because existing datasets do not permit a comprehensive evaluation of the fine-grained visual linguistic abilities of VLMs across multiple languages. To further explore the strengths of VLMs, such as GPT-4V \cite{openai2023GPT4}, we developed new datasets for the systematic and qualitative analysis of VLMs. Our contribution is four-fold: 1) we introduced nine vision-and-language (VL) tasks (including object recognition, image-text matching, and more) and constructed multilingual visual-text datasets in four languages: English, Japanese, Swahili, and Urdu through utilizing templates containing \textit{questions} and prompting GPT4-V to generate the \textit{answers} and the \textit{rationales}, 2) introduced a new VL task named \textit{unrelatedness}, 3) introduced rationales to enable human understanding of the VLM reasoning process, and 4) employed human evaluation to measure the suitability of proposed datasets for VL tasks. We show that VLMs can be fine-tuned on our datasets. Our work is the first to conduct such analyses in Swahili and Urdu. Also, it introduces \textit{rationales} in VL analysis, which played a vital role in the evaluation.

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