no code implementations • 30 Apr 2024 • Xiaoming Liu, Chen Liu, Zhaohan Zhang, Chengzhengxu Li, Longtian Wang, Yu Lan, Chao Shen
Large language models have shown their ability to become effective few-shot learners with prompting, revoluting the paradigm of learning with data scarcity.
no code implementations • 1 Feb 2024 • Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned detectors by contrastive learning on selective perturbation.
1 code implementation • 14 Aug 2023 • Chengzhengxu Li, Xiaoming Liu, Yichen Wang, Duyi Li, Yu Lan, Chao Shen
However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective.