Zobrazeno 1 - 10
of 201
pro vyhledávání: '"He, Junxian"'
Large Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps. Despite their success in various domains like mathematical problem-solving and coding, LLMs face challenge
Externí odkaz:
http://arxiv.org/abs/2410.17195
Objective: Micro-navigation poses challenges for blind and visually impaired individuals. They often need to ask for sighted assistance. We explored the feasibility of utilizing ChatGPT as a virtual assistant to provide navigation directions. Methods
Externí odkaz:
http://arxiv.org/abs/2408.08321
While large language models (LLMs) have demonstrated remarkable abilities across various fields, hallucination remains a significant challenge. Recent studies have explored hallucinations through the lens of internal representations, proposing mechan
Externí odkaz:
http://arxiv.org/abs/2407.08582
The capability to reason from text is crucial for real-world NLP applications. Real-world scenarios often involve incomplete or evolving data. In response, individuals update their beliefs and understandings accordingly. However, most existing evalua
Externí odkaz:
http://arxiv.org/abs/2406.19764
Solving mathematical problems requires advanced reasoning abilities and presents notable challenges for large language models. Previous works usually synthesize data from proprietary models to augment existing datasets, followed by instruction tuning
Externí odkaz:
http://arxiv.org/abs/2407.13690
Autor:
Ding, Wenxuan, Wang, Weiqi, Kwok, Sze Heng Douglas, Liu, Minghao, Fang, Tianqing, Bai, Jiaxin, Liu, Xin, Yu, Changlong, Li, Zheng, Luo, Chen, Yin, Qingyu, Yin, Bing, He, Junxian, Song, Yangqiu
Enhancing Language Models' (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks. However, previous approaches that distill intentions from LMs often fail to gene
Externí odkaz:
http://arxiv.org/abs/2406.10173
There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the developme
Externí odkaz:
http://arxiv.org/abs/2404.09937
Large language models (LLMs) frequently hallucinate and produce factual errors, yet our understanding of why they make these errors remains limited. In this study, we delve into the underlying mechanisms of LLM hallucinations from the perspective of
Externí odkaz:
http://arxiv.org/abs/2403.01548
Autor:
Hu, Zhiyuan, Liu, Chumin, Feng, Xidong, Zhao, Yilun, Ng, See-Kiong, Luu, Anh Tuan, He, Junxian, Koh, Pang Wei, Hooi, Bryan
In the face of uncertainty, the ability to *seek information* is of fundamental importance. In many practical applications, such as medical diagnosis and troubleshooting, the information needed to solve the task is not initially given and has to be a
Externí odkaz:
http://arxiv.org/abs/2402.03271
Autor:
Ma, Chang, Zhang, Junlei, Zhu, Zhihao, Yang, Cheng, Yang, Yujiu, Jin, Yaohui, Lan, Zhenzhong, Kong, Lingpeng, He, Junxian
Evaluating large language models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications. However, the evaluation process presents substantial challenges. A pri
Externí odkaz:
http://arxiv.org/abs/2401.13178