How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark

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 accurately inferring human intentions, beliefs, and other mental states remains underexplored. In this work, we propose an open-ended question framework to comprehensively evaluate VLMs' performance across diverse categories of ToM tasks. We curated and annotated a benchmark dataset composed of 30 images. We then assessed the performance of four VLMs of varying sizes on this dataset. Our experimental results show that the GPT-4 model outperformed all others, with only one smaller model, GPT-4o-mini, achieving comparable performance. Additionally, we observed that VLMs often struggle to accurately infer intentions in complex scenarios such as bullying or cheating. Moreover, our findings also reveal that smaller models can sometimes infer correct intentions despite relying on incorrect visual cues.