ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools

• URL: http://arxiv.org/abs/2508.03284v1
• Date: Tue, 05 Aug 2025 10:06:16 GMT
• Title: ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
• Authors: Shaofeng Yin, Ting Lei, Yang Liu,
• Abstract summary: We introduce ToolVQA, a large-scale multimodal dataset comprising 23K instances, designed to bridge the gap in real-world tool-use proficiency.<n>ToolVQA features real-world visual contexts and challenging implicit multi-step reasoning tasks, better aligning with real user interactions.<n>To construct this dataset, we propose ToolEngine, a novel data generation pipeline that employs Depth-First Search (DFS) with a dynamic in-context example matching mechanism.
• Score: 9.788417605537965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks reveal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings requiring multi-step reasoning. In this work, we introduce ToolVQA, a large-scale multimodal dataset comprising 23K instances, designed to bridge this gap. Unlike previous datasets that rely on synthetic scenarios and simplified queries, ToolVQA features real-world visual contexts and challenging implicit multi-step reasoning tasks, better aligning with real user interactions. To construct this dataset, we propose ToolEngine, a novel data generation pipeline that employs Depth-First Search (DFS) with a dynamic in-context example matching mechanism to simulate human-like tool-use reasoning. ToolVQA encompasses 10 multimodal tools across 7 diverse task domains, with an average inference length of 2.78 reasoning steps per instance. The fine-tuned 7B LFMs on ToolVQA not only achieve impressive performance on our test set but also surpass the large close-sourced model GPT-3.5-turbo on various out-of-distribution (OOD) datasets, demonstrating strong generalizability to real-world tool-use scenarios.

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