标题： 莫达：用于视觉语言模型的自动化预训练模型选择 显示英文标题

标题： Mordal: Automated Pretrained Model Selection for Vision Language Models

摘要： 将多模态数据整合到大型语言模型（LLMs）中是一种有效增强其理解非文本数据能力的方法，使其能够执行多模态任务。由于具有许多实际应用场景（如医疗保健、机器人技术和无障碍领域），视觉语言模型（VLMs）已成为多模态模型中增长最快的类别。 然而不幸的是，尽管文献中的不同VLM在不同的基准测试中展示了令人印象深刻的视觉能力，但它们是由人类专家手工设计的；目前尚无自动化框架可以创建针对特定任务的多模态模型。 我们介绍了Mordal，这是一种自动化的多模态模型搜索框架，能够在无需人工干预的情况下高效地找到适合用户定义任务的最佳VLM。 Mordal通过在搜索过程中减少需要考虑的候选者数量以及最小化评估每个剩余候选者所需的时间来实现这一目标。 我们的评估表明，与网格搜索相比，Mordal可以使用低至$8.9\times$--$11.6\times$倍的GPU小时数找到给定问题的最佳VLM。 在我们的评估过程中，我们还发现了性能优于现有最先进水平的新VLM。 显示英文摘要

摘要： Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks. Vision language models (VLMs) form the fastest growing category of multimodal models because of their many practical use cases, including in healthcare, robotics, and accessibility. Unfortunately, even though different VLMs in the literature demonstrate impressive visual capabilities in different benchmarks, they are handcrafted by human experts; there is no automated framework to create task-specific multimodal models. We introduce Mordal, an automated multimodal model search framework that efficiently finds the best VLM for a user-defined task without manual intervention. Mordal achieves this both by reducing the number of candidates to consider during the search process and by minimizing the time required to evaluate each remaining candidate. Our evaluation shows that Mordal can find the best VLM for a given problem using up to $8.9\times$--$11.6\times$ lower GPU hours than grid search. In the process of our evaluation, we have also discovered new VLMs that outperform their state-of-the-art counterparts.