7 papers · 1 filter
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
Tuo Liang, Zhe Hu, Disheng Liu +2
Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communic…
VIVA+: Human-Centered Situational Decision-Making
Zhe Hu, Yixiao Ren, Guanzhong Liu +2
Multimodal Large Language Models (MLLMs) show promising results for embodied agents in operating meaningfully in complex, human-centered environments. Yet, evaluating their capacit…
Praxis-VLM: Vision-Grounded Decision Making via Text-Driven Reinforcement Learning
Zhe Hu, Jing Li, Zhongzhu Pu +2
Vision Language Models exhibit impressive performance for various tasks, yet they often lack the sophisticated situational reasoning required for complex decision-making. This pape…
VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values
Zhe Hu, Yixiao Ren, Jing Li +1
Large vision language models (VLMs) have demonstrated significant potential for integration into daily life, making it crucial for them to incorporate human values when making deci…
Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation
Zhe Hu, Hou Pong Chan, Jing Li +1
Writing persuasive arguments is a challenging task for both humans and machines. It entails incorporating high-level beliefs from various perspectives on the topic, along with deli…
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Zhe Hu, Tuo Liang, Jing Li +5
Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding t…