Vision Language Models See What You Want but not What You See

• URL: http://arxiv.org/abs/2410.00324v1
• Date: Tue, 1 Oct 2024 01:52:01 GMT
• Title: Vision Language Models See What You Want but not What You See
• Authors: Qingying Gao, Yijiang Li, Haiyun Lyu, Haoran Sun, Dezhi Luo, Hokin Deng,
• Abstract summary: Knowing others' intentions and taking others' perspectives are two core components of human intelligence. In this paper, we investigate intentionality understanding and perspective-taking in Vision Language Models. Surprisingly, we find VLMs achieving high performance on intentionality understanding but lower performance on perspective-taking.
• Score: 9.268588981925234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowing others' intentions and taking others' perspectives are two core components of human intelligence that are typically considered to be instantiations of theory-of-mind. Infiltrating machines with these abilities is an important step towards building human-level artificial intelligence. Recently, Li et al. built CogDevelop2K, a data-intensive cognitive experiment benchmark to assess the developmental trajectory of machine intelligence. Here, to investigate intentionality understanding and perspective-taking in Vision Language Models, we leverage the IntentBench and PerspectBench of CogDevelop2K, which contains over 300 cognitive experiments grounded in real-world scenarios and classic cognitive tasks, respectively. Surprisingly, we find VLMs achieving high performance on intentionality understanding but lower performance on perspective-taking. This challenges the common belief in cognitive science literature that perspective-taking at the corresponding modality is necessary for intentionality understanding.

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