ElectroVizQA：多模态大语言模型在电子学视觉问答中的表现如何？
ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?
AI总结：
本文提出首个数字电子学视觉问答基准数据集ElectroVizQA，含约626个问题，评估多模态大语言模型在本科数字电路问题上的理解与解决能力，揭示其局限并推动工程教育应用。
摘要：
多模态大语言模型（MLLMs）因其处理多模态数据的能力而受到广泛关注，能够为复杂问题提供增强的上下文理解。MLLMs在视觉问答（VQA）等任务中展现了卓越的能力；然而，它们常常在基础工程问题上表现不佳，并且缺乏用于数字电子学等主题训练的专业数据集。为解决这一差距，我们提出了一个名为ElectroVizQA的基准数据集，专门用于评估MLLMs在本科课程中常见的数字电子电路问题上的表现。该数据集是首个针对数字电子学VQA任务定制的数据集，包含约626个视觉问题，全面覆盖了数字电子学主题。本文严格评估了MLLMs在多大程度上能够理解和解决数字电子电路问题，提供了它们在该专业领域内能力与局限性的见解。通过引入这一基准数据集，我们旨在推动MLLMs在工程教育中应用的进一步研究与发展，最终弥合性能差距并提升这些模型在技术领域的效能。
英文摘要：
Multi-modal Large Language Models (MLLMs) are gaining significant attention for their ability to process multi-modal data, providing enhanced contextual understanding of complex problems. MLLMs have demonstrated exceptional capabilities in tasks such as Visual Question Answering (VQA); however, they often struggle with fundamental engineering problems, and there is a scarcity of specialized datasets for training on topics like digital electronics. To address this gap, we propose a benchmark dataset called ElectroVizQA specifically designed to evaluate MLLMs' performance on digital electronic circuit problems commonly found in undergraduate curricula. This dataset, the first of its kind tailored for the VQA task in digital electronics, comprises approximately 626 visual questions, offering a comprehensive overview of digital electronics topics. This paper rigorously assesses the extent to which MLLMs can understand and solve digital electronic circuit questions, providing insights into their capabilities and limitations within this specialized domain. By introducing this benchmark dataset, we aim to motivate further research and development in the application of MLLMs to engineering education, ultimately bridging the performance gap and enhancing the efficacy of these models in technical fields.
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