M3UCD: A Multi-task Multimodal Metaphor Understanding Challenge Dataset for LLMs

Tianlong Zheng; Yating Yang; Rui Dong; Bo Ma; Lei Wang; Xi Zhou; Siru Miao; Turghun Osman

AAAI 2026technical0 citations

M3UCD: A Multi-task Multimodal Metaphor Understanding Challenge Dataset for LLMs

Tianlong Zheng, Yating Yang, Rui Dong, Bo Ma, Lei Wang, Xi Zhou, Siru Miao, Turghun Osman

Abstract

Understanding multimodal metaphors represents a crucial pathway for machines to comprehend human cognition. However, current research remains constrained by superficial dataset annotations, insufficient systematic evaluation of large language models, and fragmented task frameworks. To bridge these gaps, the paper proposes a systematic solution featuring: (I) We present the largest fine-grained Multi-task Multimodal Metaphor Understanding Challenge Dataset (M3UCD) built via multi-perspective collaborative annotation. It contains 15,345 samples, each annotated with 12 manual attribute labels. (II) Systematic benchmarking of LLMs

BibTeX

@inproceedings{aaai2026_m3ucdamultitaskm,
  title = {M3UCD: A Multi-task Multimodal Metaphor Understanding Challenge Dataset for LLMs},
  author = {Tianlong Zheng and Yating Yang and Rui Dong and Bo Ma and Lei Wang and Xi Zhou and Siru Miao and Turghun Osman},
  booktitle = {AAAI 2026},
  year = {2026}
}