1 code implementation • 24 Jan 2024 • Xinghao Wang, Junliang He, Pengyu Wang, Yunhua Zhou, Tianxiang Sun, Xipeng Qiu
These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e. g., semantic textual similarity (STS) tasks.
1 code implementation • 20 Jan 2024 • Pengyu Wang, Dong Zhang, Linyang Li, Chenkun Tan, Xinghao Wang, Ke Ren, Botian Jiang, Xipeng Qiu
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications.
no code implementations • 4 Feb 2023 • Bohan Li, Xiao Xu, Xinghao Wang, Yutai Hou, Yunlong Feng, Feng Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
1 code implementation • COLING 2022 • Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che
Prompting method is regarded as one of the crucial progress for few-shot nature language processing.
1 code implementation • 2 Mar 2022 • Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang
We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.
1 code implementation • 17 Jun 2021 • Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang
We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.