1 citations · 1 across the 4 of their papers we have counts for
5 papers
LiveK12Bench: Have Large Multimodal Models Truly Conquered High School-level Examinations?
Xiaohan Wang, Mingze Yin, Yilin Zhao +2
Advanced Large Multimodal Models (LMMs) have demonstrated impressive performance in K-12 reasoning tasks, exhibiting great promise as intelligent tutors. Realizing this potential r…
Chip-Tuning: Classify Before Language Models Say
Fangwei Zhu, Dian Li, Jiajun Huang +3
The rapid development in the performance of large language models (LLMs) is accompanied by the escalation of model size, leading to the increasing cost of model training and infere…
Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps
Han Wang, Yilin Zhao, Dian Li +4
Humor is previously regarded as a gift exclusive to humans for the following reasons. Humor is a culturally nuanced aspect of human language, presenting challenges for its understa…
Video-CCAM: Enhancing Video-Language Understanding with Causal Cross-Attention Masks for Short and Long Videos
Jiajun Fei, Dian Li, Zhidong Deng +3
Multi-modal large language models (MLLMs) have demonstrated considerable potential across various downstream tasks that require cross-domain knowledge. MLLMs capable of processing…
MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation
Xiaohan Wang, Dian Li, Yilin Zhao +2
Utilizing tools with Large Language Models (LLMs) is essential for grounding AI agents in real-world applications. The prevailing approach involves few-shot prompting with demonstr…