no code implementations • 24 Feb 2024 • Xia Liang, Xingjian Du, Jiaju Lin, Pei Zou, Yuan Wan, Bilei Zhu
Large Language Models (LLM) have shown encouraging progress in multimodal understanding and generation tasks.
no code implementations • 16 Oct 2023 • Xingjian Du, Zhesong Yu, Jiaju Lin, Bilei Zhu, Qiuqiang Kong
However, previous music tagging research primarily focuses on close-set music tagging tasks which can not be generalized to new tags.
1 code implementation • 8 Aug 2023 • Jiaju Lin, Haoran Zhao, Aochi Zhang, Yiting Wu, Huqiuyue Ping, Qin Chen
With ChatGPT-like large language models (LLM) prevailing in the community, how to evaluate the ability of LLMs is an open question.
1 code implementation • 5 Aug 2023 • Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie zhou, Liang He, Xipeng Qiu
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
5 code implementations • 22 May 2023 • Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Jiaju Lin, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Bolun Wang, Johan S. Wind, Stanislaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.
Ranked #22 on Natural Language Inference on WNLI
no code implementations • 4 Oct 2022 • Jiaju Lin, Jie zhou, Qin Chen
Prompt-based methods have become increasingly popular among information extraction tasks, especially in low-data scenarios.
1 code implementation • 1 May 2022 • Jiaju Lin, Qin Chen, Jie zhou, Jian Jin, Liang He
Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document.
no code implementations • 11 Sep 2021 • Jiaju Lin, Qin Chen
Eliciting knowledge from pre-trained language models via prompt-based learning has shown great potential in many natural language processing tasks.
no code implementations • SEMEVAL 2021 • Jiaju Lin, Jing Ling, Zhiwei Wang, Jiawei Liu, Qin Chen, Liang He
The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph.