VIVA：面向人类价值观驱动的视觉落地决策的基准测试
VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values
AI总结：
该研究提出名为VIVA的人类价值观驱动视觉落地决策基准，含1240张现实场景图与人工标注决策，实验发现大型视觉语言模型在此类多模态决策上存在局限，并指出利用行动后果与预测价值观的潜在价值。
摘要：
大型视觉语言模型（VLM）已展现出融入日常生活的巨大潜力，因此它们在现实场景中做决策时融入人类价值观至关重要。本文提出VIVA，即人类价值观驱动的视觉落地决策基准测试。多数大型VLM聚焦于物理层面的技能，本研究首次检验了这类模型在视觉呈现的场景下，利用人类价值观做决策的多模态能力。VIVA包含1240张展现各类现实场景的图像，以及基于这些图像人工标注的决策结果。针对其中给定的图像，模型需选出最适合应对该场景的行动，并给出相关的人类价值观以及决策背后的理由。基于VIVA的大量实验表明，VLM在利用人类价值观做出多模态决策方面存在局限。进一步分析显示，利用行动后果和预测的人类价值观具有潜在益处。
英文摘要：
Large vision language models (VLMs) have demonstrated significant potential for integration into daily life, making it crucial for them to incorporate human values when making decisions in real-world situations. This paper introduces VIVA, a benchmark for VIsion-grounded decision-making driven by human VAlues. While most large VLMs focus on physical-level skills, our work is the first to examine their multimodal capabilities in leveraging human values to make decisions under a vision-depicted situation. VIVA contains 1,240 images depicting diverse real-world situations and the manually annotated decisions grounded in them. Given an image there, the model should select the most appropriate action to address the situation and provide the relevant human values and reason underlying the decision. Extensive experiments based on VIVA show the limitation of VLMs in using human values to make multimodal decisions. Further analyses indicate the potential benefits of exploiting action consequences and predicted human values.
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。
