视觉语言模型能多好地理解人类意图？一个开放式心理理论问题评估基准
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark
AI总结：
提出开放式心理理论问题评估基准，测试四个视觉语言模型推断人类意图的能力，发现GPT-4最优，且小模型可能依赖错误线索却得出正确意图。
摘要：
视觉语言模型（VLMs）在视觉问答（VQA）任务中已展现出强大的推理能力；然而，它们在执行心理理论（ToM）任务（如推断人类意图、信念和心理状态）方面的能力仍未得到充分探索。我们提出了一个开放式问题框架，用于评估VLMs在不同类别ToM任务中的表现。我们策划并标注了一个包含30张图像的基准数据集，并评估了四个不同规模的VLMs的性能。我们的结果表明，GPT-4模型优于所有其他模型，只有一个较小的模型GPT-4o-mini达到了相当的性能。我们观察到，VLMs在复杂场景（如欺凌或作弊）中推断意图时常常遇到困难。我们的发现揭示，较小的模型有时能推断出正确的意图，尽管依赖于错误的视觉线索。该数据集可在https://github.com/ximingwen/ToM-AAAI25-Multimodal获取。
英文摘要：
Vision Language Models (VLMs) have demonstrated strong reasoning capabilities in Visual Question Answering (VQA) tasks; however, their ability to perform Theory of Mind (ToM) tasks, such as inferring human intentions, beliefs, and mental states, remains underexplored. We propose an open-ended question framework to evaluate VLMs' performance across diverse categories of ToM tasks. We curated and annotated a benchmark dataset of 30 images and evaluated the performance of four VLMs of varying sizes. Our results show that the GPT-4 model outperformed all the others, with only one smaller model, GPT-4o-mini, achieving comparable performance. We observed that VLMs often struggle to infer intentions in complex scenarios such as bullying or cheating. Our findings reveal that smaller models can sometimes infer correct intentions despite relying on incorrect visual cues. The dataset is available at https://github.com/ximingwen/ToM-AAAI25-Multimodal.
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。
