no code implementations • 1 Apr 2024 • Fang Liu, Yang Liu, Lin Shi, Houkun Huang, Ruifeng Wang, Zhen Yang, Li Zhang
The rise of Large Language Models (LLMs) has significantly advanced many applications on software engineering tasks, particularly in code generation.
1 code implementation • 13 Mar 2024 • Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities.
no code implementations • 12 Mar 2024 • Yan Liu, Renren Jin, Lin Shi, Zheng Yao, Deyi Xiong
We conduct extensive experiments on a wide range of LLMs on FineMath and find that there is still considerable room for improvements in terms of mathematical reasoning capability of Chinese LLMs.
no code implementations • 17 Dec 2023 • Moshi Wei, Nima Shiri Harzevili, Alvine Boaye Belle, Junjie Wang, Lin Shi, Jinqiu Yang, Song Wang, Ming Zhen, Jiang
We also investigate the typical data extraction procedures and collection approaches employed by the existing approaches.
no code implementations • 6 Dec 2022 • Lin Shi, Bei Peng
In multi-agent reinforcement learning (MARL), many popular methods, such as VDN and QMIX, are susceptible to a critical multi-agent pathology known as relative overgeneralization (RO), which arises when the optimal joint action's utility falls below that of a sub-optimal joint action in cooperative tasks.
1 code implementation • 14 Sep 2022 • Fangwen Mu, Xiao Chen, Lin Shi, Song Wang, Qing Wang
Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process.
no code implementations • 12 Aug 2021 • Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, Qing Wang
In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents.
no code implementations • 20 Mar 2020 • Anthony Hei-Long Chan, Yishan Luo, Lin Shi, Ronald Lok-Ming Lui
Using 110 NC subjects and 110 AD patients from the ADNI database, the proposed algorithm achieves 85:2% testing accuracy on 80 random samples as testing subjects, with the incorporation of surface geometry in the classification machine.