1 code implementation • 16 Feb 2024 • Yuanzhen Xie, Xinzhou Jin, Tao Xie, Mingxiong Lin, Liang Chen, Chenyun Yu, Lei Cheng, Chengxiang Zhuo, Bo Hu, Zang Li
To improve the contextual learning capabilities of LLMs in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition.
no code implementations • 29 Dec 2023 • Shaojie Zhu, Zhaobin Wang, Chengxiang Zhuo, Hui Lu, Bo Hu, Zang Li
CoT (Chain-of-Thought) is a way to solve reasoning problems for LLMs .
no code implementations • 23 May 2023 • Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, Zang Li
At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks.
1 code implementation • 22 Nov 2022 • Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, XiaoHu Qie, Di Niu
CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.
2 code implementations • 13 Oct 2022 • Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, XiaoHu Qie
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.
no code implementations • 13 Jun 2022 • Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li
That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.
1 code implementation • 19 Dec 2019 • Huiting Hong, Hantao Guo, Yu-Cheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.
no code implementations • 14 Oct 2019 • Hao Cheng, Xiaoqing Yang, Zang Li, Yanghua Xiao, Yu-Cheng Lin
Deep neural networks have been widely used in text classification.
no code implementations • 20 Aug 2019 • Yu-Cheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye
In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks.