no code implementations • 31 May 2024 • Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, Ruifeng Xu
Prior RAG studies on the robustness of retrieval noises often confine themselves to a limited set of noise types, deviating from real-world retrieval environments and limiting practical applicability.
no code implementations • 26 Mar 2024 • Yuelin Bai, Xinrun Du, Yiming Liang, Yonggang Jin, Ziqiang Liu, Junting Zhou, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Wenhu Chen, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset.
1 code implementation • 26 Feb 2024 • Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, BoWen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu, Ye Li, Jianping Fan
In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain.
no code implementations • 26 Feb 2024 • Shiwen Ni, Min Yang, Ruifeng Xu, Chengming Li, Xiping Hu
To solve the inconsistency between training and inference caused by the randomness of dropout, some studies use consistency training to regularize dropout at the output layer.
no code implementations • 10 Feb 2024 • Zhibo Chu, Shiwen Ni, Zichong Wang, Xi Feng, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang, Wenbin Zhang
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.
1 code implementation • 29 Jan 2024 • Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang
The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain.
no code implementations • 14 Nov 2023 • Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge.
1 code implementation • 17 Jul 2022 • Shiwen Ni, Hung-Yu Kao
Numerically, compared to MLM-RoBERTa-large and MLM-BERT-large, our RTD-ELECTRA-large has an average of about 8. 4% and 13. 7% improvement on all 15 tasks.
no code implementations • 1 Dec 2021 • Shiwen Ni, Jiawen Li, Hung-Yu Kao
It is difficult for humans to distinguish the true and false of rumors, but current deep learning models can surpass humans and achieve excellent accuracy on many rumor datasets.
1 code implementation • 29 Aug 2021 • Shiwen Ni, Jiawen Li, Hung-Yu Kao
We compare the proposed method with other adversarial training methods and regularization methods, and our method achieves state-of-the-art on all datasets.
no code implementations • 29 Aug 2021 • Shiwen Ni, Jiawen Li, Hung-Yu Kao
As such, the robustness and generalization of the current rumor detection model are put into question.