Multimodal large language models (MLLMs) have shown strong potential in open-ended emotion understanding, yet they often generate emotion hallucinations. Evaluating such hallucinations is particularly challenging for two reasons. First, emotion understanding spans multiple cognitive facets, from multimodal perception t...
Bo-Wen Zeng, Pei-Pei Song, Wei-Dong Chen et al.· 0 citations
A Naturalness-guided Manifold Flow Matching framework is proposed, termed \textbf{SignNMFlow}, which constructs conditional paths directly on the motion manifold by jointly considering geometric efficiency and the motion distribution, and introduces a motion naturalness measure to characterize the motion distribution.
Jia-Yi He, Shen-Geng Tang, Si-Si You et al.· 0 citations
Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
A. Cioppa, Silvio Giancola, Haakan Ardo et al.· 1 citation
A rationale-guided knowledge distillation framework for cross-lingual stance detection using Chain-of-Thought prompting to guide Large Language Models in generating informative rationales, and distill the resulting reasoning knowledge into a compact student model.
