no code implementations • 28 Mar 2024 • Deyuan Liu, Zecheng Wang, Bingning Wang, WeiPeng Chen, Chunshan Li, Zhiying Tu, Dianhui Chu, Bo Li, Dianbo Sui
The rapid proliferation of large language models (LLMs) such as GPT-4 and Gemini underscores the intense demand for resources during their training processes, posing significant challenges due to substantial computational and environmental costs.
1 code implementation • 4 Feb 2024 • Jiahao Wang, Bolin Zhang, Qianlong Du, Jiajun Zhang, Dianhui Chu
Instruction tuning is a vital step of training large language models (LLM), so how to enhance the effect of instruction tuning has received increased attention.
1 code implementation • 14 Dec 2023 • Guoqing Chao, Yi Jiang, Dianhui Chu
In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC).
1 code implementation • 29 Jan 2023 • Bolin Zhang, Yunzhe Xu, Zhiying Tu, Dianhui Chu
Specifically, the retrieval performance is improved while the model size is reduced by training two lightweight, task-specific adapter modules that share only one underlying T5-Encoder model.
no code implementations • 29 Mar 2022 • Bolin Zhang, Zhiying Tu, Yunzhe Xu, Dianhui Chu, Xiaofei Xu
To this end, two phases must be applied: I. Sequence planning and Real-time detection of user requirement, II. Service resource selection and Response generation.