AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models

• URL: http://arxiv.org/abs/2406.09295v3
• Date: Wed, 04 Jun 2025 07:53:00 GMT
• Title: AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models
• Authors: Yuhang Wu, Wenmeng Yu, Yean Cheng, Yan Wang, Xiaohan Zhang, Jiazheng Xu, Ming Ding, Yuxiao Dong,
• Abstract summary: We introduce AlignMMBench, a benchmark for evaluating the alignment capabilities of large Vision-Language Models (VLMs)<n>This benchmark is meticulously curated from real-world scenarios and internet sources, and includes both single-turn and multi-turn dialogue scenarios.<n>We also develop CritiqueVLM, a rule-calibrated evaluator that exceeds GPT-4's evaluation ability.
• Score: 34.843603169616486
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Evaluating the alignment capabilities of large Vision-Language Models (VLMs) is essential for determining their effectiveness as helpful assistants. However, existing benchmarks primarily focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. In this paper, we address this gap by introducing AlignMMBench, which provides more nuanced evaluations of alignment capabilities and is the first benchmark specifically designed for Chinese visual contexts. This benchmark is meticulously curated from real-world scenarios and internet sources, encompassing thirteen specific tasks across three categories, and includes both single-turn and multi-turn dialogue scenarios. Incorporating a prompt rewrite strategy, AlignMMBench encompasses 1,054 images and 4,978 question-answer pairs. To facilitate the evaluation pipeline, we develop CritiqueVLM, a rule-calibrated evaluator that exceeds GPT-4's evaluation ability. Additionally, we measure the "alignment score", a quantitative metric designed to assess the robustness and stability of models across diverse prompts. Finally, we evaluate the performance of representative VLMs on AlignMMBench, offering insights into the capabilities and limitations of different VLM architectures. The evaluation code and data are available at https://github.com/THUDM/AlignMMBench.

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