1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models
Zhaolu Kang, Yidi Wang, Yile Li +4
Ambiguity is a central feature of natural communication: a sentence, image, or sound may remain underspecified until evidence from the other channels is considered. Yet current mul…
Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning
Zhaolu Kang, Meixin Wu, Yu Xue +6
Omni-modal large language models are increasingly evaluated on clean text--vision--audio inputs, where every channel is present, synchronized, and readily interpretable. Such score…
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models
Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai +8
Multimodal Large Language Models (MLLMs) have demonstrated significant advances across numerous vision-language tasks. MLLMs have shown promising capability in aligning visual and…
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
Zhaolu Kang, Junhao Gong, Jiaxu Yan +15
Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchmarks for evaluating MLLMs primarily emp…
JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning
Zhaolu Kang, Hongtian Cai, Xiangyang Ji +2
In recent years, Unsupervised Domain Adaptation (UDA) has gained significant attention in the field of Natural Language Processing (NLP) owing to its ability to enhance model gener…