Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans

• URL: http://arxiv.org/abs/2505.11141v2
• Date: Sat, 24 May 2025 02:18:52 GMT
• Title: Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
• Authors: Yansheng Qiu, Li Xiao, Zhaopan Xu, Pengfei Zhou, Zheng Wang, Kaipeng Zhang,
• Abstract summary: We propose Human-Aligned Bench, a benchmark for alignment of multimodal reasoning with human performance.<n>We collected 9,794 multimodal questions that solely rely on contextual reasoning, including bilingual (Chinese and English) multimodal questions and pure text-based questions.<n>Extensive experiments on the Human-Aligned Bench reveal notable differences between the performance of current MLLMs in multimodal reasoning and human performance.
• Score: 9.315735862658244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of achieving Artificial General Intelligence (AGI) is to imitate humans and surpass them. Models such as OpenAI's o1, o3, and DeepSeek's R1 have demonstrated that large language models (LLMs) with human-like reasoning capabilities exhibit exceptional performance and are being gradually integrated into multimodal large language models (MLLMs). However, whether these models possess capabilities comparable to humans in handling reasoning tasks remains unclear at present. In this paper, we propose Human-Aligned Bench, a benchmark for fine-grained alignment of multimodal reasoning with human performance. Specifically, we collected 9,794 multimodal questions that solely rely on contextual reasoning, including bilingual (Chinese and English) multimodal questions and pure text-based questions, encompassing four question types: visual reasoning, definition judgment, analogical reasoning, and logical judgment. More importantly, each question is accompanied by human success rates and options that humans are prone to choosing incorrectly. Extensive experiments on the Human-Aligned Bench reveal notable differences between the performance of current MLLMs in multimodal reasoning and human performance. The findings on our benchmark provide insights into the development of the next-generation models.

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