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Creativity is a fundamental aspect of intelligence, involving the ability to generate novel and appropriate solutions across diverse contexts. While Large Language Models (LLMs) have been extensively evaluated for their creative capabilities, the assessment of Multimodal Large Language Models (MLLMs) in this domain remains largely unexplored. To address this gap, we introduce Creation-MMBench, a multimodal benchmark specifically designed to evaluate the creative capabilities of MLLMs in real-world, image-based tasks. The benchmark comprises 765 test cases spanning 51 fine-grained tasks. To ensure rigorous evaluation, we define instance-specific evaluation criteria for each test case, guiding the assessment of both general response quality and factual consistency with visual inputs. Experimental results reveal that current open-source MLLMs significantly underperform compared to proprietary models in creative tasks. Furthermore, our analysis demonstrates that visual fine-tuning can negatively impact the base LLM's creative abilities. Creation-MMBench provides valuable insights for advancing MLLM creativity and establishes a foundation for future improvements in multimodal generative intelligence. Full data and evaluation code is released on Creation-MMBench.
1. Lack of Multimodal Creative Benchmarks:
As a well-established theory in psychology, the Triarchic Theory of Intelligence comprises three subtheories: the analytical subtheory, the contextual subtheory, and the creative subtheory. The analytical subtheory primarily focuses on information processing and problem-solving skills based on domain-specific knowledge and can be assessed through various knowledge and reasoning benchmarks. The contextual subtheory, on the other hand, emphasizes practical intelligence in real-world scenarios and is typically evaluated using agent-based or embodied AI benchmarks. Despite the significance of the creative subtheory in intelligence, evaluations of MLLMs' creative capabilities remain highly inadequate and lag significantly behind those conducted for LLMs.
2. Limited Capabilities of Existing Benchmarks:
MLLMs have certain shortcomings in dealing with creative tasks in daily situation. However, existing benchmarks feature simple questions that fail to assess model performance in real-life creative tasks.
Overview of Creation-MMBench. Contain four task categories: Literary Writing, Common Functional Writing, Professional Functional Writing, and Creative Multimodal Understanding. Each category consists of multiple tasks, and the types of images are diverse. Only a few representative tasks of each category are shown here. Complete list of tasks is detailed in Appendix A.
Comparison of Creation-MMBench with other partial-creation MLLM benchmarks:
Statistics and Cases of Creation-MMBench:
(a) Distribution of query lengths.
(b) Roles in Creation-MMBench.
(c) Example Case of Creation-MMBench.
Statistics and Cases of Creation-MMBench: Compared to other widely used MLLM benchmarks, Creation-MMBench features a more comprehensive query design to capture abundant creative contexts. Diverse roles are introduced into the queries to stimulate MLLMs' utilization of disciplinary and prior knowledge. As an MLLM benchmark, Creation-MMBench includes a rich variety of images to thoroughly evaluate multiple capabilities of MLLMs.
VFS stands for Visual Factuality Score. LW, CFW, PFW, and CMU stand for four categories in Creation-MMBench: Literary Writing, Common Functional Writing, Professional Functional Writing, and Creative Multimodal Understanding.
OC Score represents the average score of the OpenVLM Leaderboard and mainly demonstrates the objective performance of the model.
The token number is calculated with tiktoken GPT-4o-1120 tokenizer.
The best results are highlighted in bold.
Comparing OC Score and Creation-MMBench Reward. This figure shows the model performance on the OpenVLM Leaderboard and Creation-MMBench, highlighting a significant gap between objective performance and visual creativity in some open-source models.
LLM performance on Creation-MMBench-TO and Visual Instruction Tuning Impact on VLM creation capability.
@misc{fang2025creationmmbench, title={Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM}, author={Xinyu Fang and Zhijian Chen and Kai Lan and Shengyuan Ding and Yingji Liang and Xiangyu Zhao and Farong Wen and Zicheng Zhang and Guofeng Zhang and Haodong Duan and Kai Chen and Dahua Lin}, year={2025}, eprint={2503.14478}, archivePrefix={arXiv}, primaryClass={cs.CV} }