评估大型视觉语言模型中的颜色视觉测试
Assessing Color Vision Test in Large Vision-language Models
AI总结：
本文针对大型视觉语言模型颜色视觉能力未被充分探索的问题，定义了颜色视觉测试任务并构建了多类别、多难度数据集，同时分析了模型错误类型并提出微调策略以提升其测试表现。
摘要：
随着大型视觉语言模型的广泛应用，这些模型的颜色视觉能力至关重要。然而，大型视觉语言模型的颜色视觉能力尚未得到充分探索。为填补这一空白，我们为大型视觉语言模型定义了一项颜色视觉测试任务，并构建了一个数据集，涵盖多个类别的测试问题和不同难度级别的任务。此外，我们分析了大型视觉语言模型所犯的错误类型，并提出了微调策略以提升其在颜色视觉测试中的表现。
英文摘要：
With the widespread adoption of large vision-language models, the capacity for color vision in these models is crucial. However, the color vision abilities of large visual-language models have not yet been thoroughly explored. To address this gap, we define a color vision testing task for large vision-language models and construct a dataset \footnote{Anonymous Github Showing some of the data https://anonymous.4open.science/r/color-vision-test-dataset-3BCD} that covers multiple categories of test questions and tasks of varying difficulty levels. Furthermore, we analyze the types of errors made by large vision-language models and propose fine-tuning strategies to enhance their performance in color vision tests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
