JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations
Xinyi Xu, Bingguang Hao, Yongyi Xiong, Zimo Chen, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou, Shihan Dou
摘要
Self-deprecation is a prevalent communicative strategy in human society, often using imagetext interplay to express emotions and intentions. Although self-deprecation is widespread in real-world conversations, the ability of multimodal large language models (MLLMs) to understand it remains underexplored. To fill this gap, we introduce JanusMM, the first benchmark designed to evaluate MLLMs' understanding of self-deprecation in real-world conversations. JanusMM contains 2,016 bilingual memes from three types of social interactions and provides a dual-task evaluation framework with six new metrics. The first task assesses MLLMs' abilities in self-deprecation recognition and reasoning, while the second task evaluates the consistency of their understanding by simulating the perspectives of the initiator and responder. We evaluate ten frontier MLLMs and find that they exhibit weak recognition and reasoning abilities, with their understanding of self-deprecation remaining inconsistent across both perspectives.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper6
- BARTScore: Evaluating Generated Text as Text GenerationWeizhe Yuan, Graham Neubig, Pengfei LiuNeurIPS 2021 · 被引用 1,143 次
- Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction TuningZebang Cheng, Zhi-Qi Cheng, Jun-Yan He, Kai Wang 等NeurIPS 2024 · 被引用 293 次
- Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support ConversationDongjin Kang, Sunghwan Kim, Taeyoon Kwon, Seungjun Moon 等ACL 2024 · 被引用 14 次
- DocMSU: A Comprehensive Benchmark for Document-Level Multimodal Sarcasm UnderstandingHang Du, Guoshun Nan, Sicheng Zhang, Binzhu Xie 等AAAI 2024 · 被引用 9 次
- DEEMO: De-identity Multimodal Emotion Recognition and ReasoningDeng Li, Bohao Xing, Xin Liu, Baiqiang Xia 等ACM MM 2025 · 被引用 8 次
相关 Paper
- EEmo-Bench: A Benchmark for Multi-modal Large Language Models on Image Evoked Emotion AssessmentLancheng Gao, Ziheng Jia, Yunhao Zeng, Wei Sun 等ACM MM 2025 · 被引用 2 次
- MetaGPT: A Large Vision-Language Model for Meme Metaphor UnderstandingBo Xu, Chenyuan Wang, Xinyu Chen, Hongfei Lin 等AAAI 2026
- MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language ModelsZhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao 等EMNLP 2025
- PunchBench: Benchmarking MLLMs in Multimodal Punchline ComprehensionKun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu 等ACL 2025 · 被引用 3 次
- EMODIS: A Benchmark for Context-Dependent Emoji Disambiguation in Large Language ModelsJiacheng Huang, Ning Yu, Xiaoyin YiAAAI 2026
