SceneGram：在场景语境中对七巧板进行概念化与描述
SceneGram: Conceptualizing and Describing Tangrams in Scene Context
AI总结：
本研究提出SceneGram数据集，收录不同场景语境下人类对七巧板形状的指称内容，用于分析场景语境对物体概念化的影响，同时发现多模态LLM生成的指称缺乏人类概念化的丰富性与多样性。
摘要：
关于指称与命名的研究表明，人类会采用截然不同的方式对同一物体进行概念化与指称，例如同一个抽象的七巧板形状可以是“螃蟹”“水槽”或“宇宙飞船”。认知科学中另一个普遍假设是，场景语境从根本上塑造了我们对物体的视觉感知与概念预期。本文提出了SceneGram——一个包含人类对置于不同场景语境下的七巧板形状的指称内容的数据集，可用于系统分析场景语境对概念化的影响。基于该数据，我们分析了多模态大语言模型（multimodal LLMs）生成的七巧板形状指称内容，结果表明这些模型并未体现出人类指称中概念化的丰富性与多样性。
英文摘要：
Research on reference and naming suggests that humans can come up with very different ways of conceptualizing and referring to the same object, e.g. the same abstract tangram shape can be a "crab", "sink" or "space ship". Another common assumption in cognitive science is that scene context fundamentally shapes our visual perception of objects and conceptual expectations. This paper contributes SceneGram, a dataset of human references to tangram shapes placed in different scene contexts, allowing for systematic analyses of the effect of scene context on conceptualization. Based on this data, we analyze references to tangram shapes generated by multimodal LLMs, showing that these models do not account for the richness and variability of conceptualizations found in human references.
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