8 papers
A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
Yue Zhang, Liqiang Jing, Jia Li +4
Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…
Fine-grained and Explainable Factuality Evaluation for Multimodal Summarization
Yue Zhang, Jingxuan Zuo, Ke Su +1
Multimodal summarization aims to generate a concise summary based on the input text and image. However, the existing methods potentially suffer from unfactual output. To evaluate t…
FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs
Bowen Yan, Zhengsong Zhang, Liqiang Jing +2
The rapid development of Large Vision-Language Models (LVLMs) often comes with widespread hallucination issues, making cost-effective and comprehensive assessments increasingly vit…
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback
Liqiang Jing, Xinya Du
Large Vision-Language Models (LVLMs) have demonstrated proficiency in tackling a variety of visual-language tasks. However, current LVLMs suffer from misalignment between text and…
DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?
Liqiang Jing, Zhehui Huang, Xiaoyang Wang +6
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents…
Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue
Kun Ouyang, Liqiang Jing, Xuemeng Song +3
Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple m…