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pro vyhledávání: '"Zhang, Wenjia"'
Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets has traditionally been expensive and laborious, often relying on manual
Externí odkaz:
http://arxiv.org/abs/2408.01323
To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quoted statements. To achieve this, we built a novel dataset, called NewsQu
Externí odkaz:
http://arxiv.org/abs/2406.11745
Understanding spatial location and relationships is a fundamental capability for modern artificial intelligence systems. Insights from human spatial cognition provide valuable guidance in this domain. Neuroscientific discoveries have highlighted the
Externí odkaz:
http://arxiv.org/abs/2406.07049
Autor:
Zhu, He, Zhang, Wenjia, Huang, Nuoxian, Li, Boyang, Niu, Luyao, Fan, Zipei, Lun, Tianle, Tao, Yicheng, Su, Junyou, Gong, Zhaoya, Fang, Chenyu, Liu, Xing
In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique cha
Externí odkaz:
http://arxiv.org/abs/2402.19273
In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives such as novels in an immersive environment. We leverage Large Language Models (LLMs) to ge
Externí odkaz:
http://arxiv.org/abs/2310.01459
The spatial photonic Ising machine has achieved remarkable advancements in solving combinatorial optimization problems. However, it still remains a huge challenge to flexibly mapping an arbitrary problem to Ising model. In this paper, we propose a ge
Externí odkaz:
http://arxiv.org/abs/2306.10076
Autor:
Cheng, Peng, Zhan, Xianyuan, Wu, Zhihao, Zhang, Wenjia, Song, Shoucheng, Wang, Han, Lin, Youfang, Jiang, Li
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends
Externí odkaz:
http://arxiv.org/abs/2306.04220
Autor:
Li, Yu, Zhang, Wenjia
In this paper, we establish the rigidity of the generalized cylinder $N^n \times \mathbb R^{m-n}$, or a quotient thereof, in the space of Ricci shrinkers equipped with the pointed-Gromov-Hausdorff topology. Here, $N$ is a stable Einstein manifold tha
Externí odkaz:
http://arxiv.org/abs/2305.06143
To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel News
Externí odkaz:
http://arxiv.org/abs/2305.04825
Autor:
Cong Xu, Zhang Wenjia
Publikováno v:
Journal of Intelligent Systems, Vol 33, Iss 1, Pp 57-76 (2024)
Geometric and floral models are an important part of clothing and have been used for thousands of years. Although the styles of geometric models and flower models have undergone changes over the centuries, they are still one of the important factors
Externí odkaz:
https://doaj.org/article/2df9ba267686400bac5c66ff70f066bc