多模态大语言模型能否解码创造性飞跃？——引入C4基准用于跨概念理解研究

摘要

多模态大语言模型（MLLMs）的创造能力在设计、传播、教育和人机协作中具有重要意义，然而与面向准确性的任务相比，其评估仍然困难，因为明确的目标和奖励信号十分稀缺。跨概念理解是接受性创造力的核心认知能力，它使感知者能够从不显而易见但有意义的概念关系中恢复出预期含义。我们将题项构建操作化为跨概念编码，将模型推理操作化为跨概念解码。我们提出了C4，一个受认知启发的基于成语（Chinese idiom）的跨概念创造力（Cross-Concept Creativity）评估框架。其编码组件沿着人工标注并经第三方审核的跨概念网络中的桥梁路径，将目标槽映射到可成像的替代概念，从而支持具有明确结构、难度以桥梁数量和深度为指标、并具备精确答案的批量生成。利用该框架，我们实例化了C4评估集（C4-Eval），包含184个合成题项和37个人类创作的跨概念成语修辞表达（收集自在线来源）。我们对所收集修辞表达的跨概念关系、桥梁路径和推理过程进行了人工构建与审核。每个C4-Eval题项在五种任务设置中被实例化，共产生884个主要答案恢复用例。在十个被评估的MLLMs中，最强的闭源模型的主要准确率分别达到50.7%和48.0%，而开源模型则显著更低。候选约束显著提高了准确率，但桥梁提示和解释请求带来的提升有限。这些结果揭示了当前MLLMs在通过跨概念关系解码创造性编码含义方面存在显著差距。代码见补充材料。

English

Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals are scarce compared with accuracy-oriented tasks. Cross-concept understanding is a core cognitive capacity underlying receptive creativity. It enables a perceiver to recover intended meaning from non-obvious but meaningful conceptual relations. We operationalize item construction as cross-concept encoding and model inference as cross-concept decoding. We introduce C4, a cognition-inspired evaluation framework for Chengyu (Chinese idiom)-based Cross-Concept Creativity. Its encoding component maps target slots to imageable substitute concepts along bridge paths in a manually annotated and third-party-reviewed cross-concept network, enabling batch generation with explicit structure, difficulty indexed by bridge count and depth, and exact answers. Using this framework, we instantiate the C4 Evaluation Set (C4-Eval), comprising 184 synthetic items and 37 human-created cross-concept chengyu figures collected from online sources. We manually construct and review cross-concept relations, bridge paths, and reasoning processes for the collected figures. Each C4-Eval item is instantiated in five task settings, yielding 884 primary answer-recovery cases. Across ten evaluated MLLMs, the strongest closed models reach 50.7% and 48.0% primary accuracy, while open-source models remain substantially lower. Candidate constraints improve accuracy sharply, but bridge hints and explanation requests provide only modest gains. These results expose a substantial gap in how current MLLMs decode creatively encoded meaning through cross-concept relations. The code is in the supplementary material.