Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Gu, Zhouhong"'
Autor:
Wang, Yifeng, Gu, Zhouhong, Zhang, Siwei, Zheng, Suhang, Wang, Tao, Li, Tianyu, Feng, Hongwei, Xiao, Yanghua
Explainable fake news detection predicts the authenticity of news items with annotated explanations. Today, Large Language Models (LLMs) are known for their powerful natural language understanding and explanation generation abilities. However, presen
Externí odkaz:
http://arxiv.org/abs/2409.01787
Autor:
Gu, Zhouhong, Zhang, Lin, Zhu, Xiaoxuan, Chen, Jiangjie, Huang, Wenhao, Zhang, Yikai, Wang, Shusen, Ye, Zheyu, Gao, Yan, Feng, Hongwei, Xiao, Yanghua
Detecting evidence within the context is a key step in the process of reasoning task. Evaluating and enhancing the capabilities of LLMs in evidence detection will strengthen context-based reasoning performance. This paper proposes a benchmark called
Externí odkaz:
http://arxiv.org/abs/2406.12641
Online courses have significantly lowered the barrier to accessing education, yet the varying content quality of these videos poses challenges. In this work, we focus on the task of automatically evaluating the quality of video course content. We hav
Externí odkaz:
http://arxiv.org/abs/2407.12005
The effective utilization of structured data, integral to corporate data strategies, has been challenged by the rise of large language models (LLMs) capable of processing unstructured information. This shift prompts the question: can LLMs interpret s
Externí odkaz:
http://arxiv.org/abs/2406.10621
Autor:
Huang, Wenhao, Gu, Zhouhong, Peng, Chenghao, Li, Zhixu, Liang, Jiaqing, Xiao, Yanghua, Wen, Liqian, Chen, Zulong
Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry efforts. Existing methods, wrappers-based methods suffer from limited ad
Externí odkaz:
http://arxiv.org/abs/2404.12753
Autor:
Gu, Zhouhong, Zhu, Xiaoxuan, Guo, Haoran, Zhang, Lin, Cai, Yin, Shen, Hao, Chen, Jiangjie, Ye, Zheyu, Dai, Yifei, Gao, Yan, Hu, Yao, Feng, Hongwei, Xiao, Yanghua
Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies. Considering that the study of how language affects human behavior needs to put
Externí odkaz:
http://arxiv.org/abs/2403.13433
Autor:
Wang, Jianchen, Gu, Zhouhong, Zhu, Xiaoxuan, Zhang, Lin, Ye, Haoning, Xiong, Zhuozhi, Feng, Hongwei, Xiao, Yanghua
Large Language Models have revolutionized numerous tasks with their remarkable efficacy. However, editing these models, crucial for rectifying outdated or erroneous information, often leads to a complex issue known as the ripple effect in the hidden
Externí odkaz:
http://arxiv.org/abs/2403.07825
Pre-trained language models (PLMs) have been prevailing in state-of-the-art methods for natural language processing, and knowledge-enhanced PLMs are further proposed to promote model performance in knowledge-intensive tasks. However, conceptual knowl
Externí odkaz:
http://arxiv.org/abs/2401.05669
Autor:
He, Qianyu, Zeng, Jie, Huang, Wenhao, Chen, Lina, Xiao, Jin, He, Qianxi, Zhou, Xunzhe, Chen, Lida, Wang, Xintao, Huang, Yuncheng, Ye, Haoning, Li, Zihan, Chen, Shisong, Zhang, Yikai, Gu, Zhouhong, Liang, Jiaqing, Xiao, Yanghua
Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions tha
Externí odkaz:
http://arxiv.org/abs/2309.09150
Autor:
Wang, Xintao, Yang, Qianwen, Qiu, Yongting, Liang, Jiaqing, He, Qianyu, Gu, Zhouhong, Xiao, Yanghua, Wang, Wei
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts
Externí odkaz:
http://arxiv.org/abs/2308.11761