Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data （2023.02.24）
Kashun Shum, Shizhe Diao, Tong Zhang . - 【ArXiv】
Guiding Large Language Models via Directional Stimulus Prompting （2023.02.22）
Zekun Li, Baolin Peng, Pengcheng He, Michel Galley, Jianfeng Gao, etc . - 【ArXiv】
Evaluating the Robustness of Discrete Prompts （2023.02.11）
Yoichi Ishibashi, D. Bollegala, Katsuhito Sudoh, Satoshi Nakamura . - 【ArXiv】
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery （2023.02.07）
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, etc . - 【ArXiv】
Ask Me Anything: A simple strategy for prompting language models （2022.10.05）
Simran Arora, A. Narayan, Mayee F. Chen, Laurel J. Orr, Neel Guha, etc . - 【ArXiv】
STaR: Bootstrapping Reasoning With Reasoning （2022.03.28）
E. Zelikman, Yuhuai Wu, Noah D. Goodman . - 【ArXiv】
Making Pre-trained Language Models Better Few-shot Learners （2021.01.01）
Tianyu Gao, Adam Fisch, Danqi Chen . - 【Annual Meeting of the Association for Computational Linguistics】
Eliciting Knowledge from Language Models Using Automatically Generated Prompts （2020.10.29）
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, Sameer Singh . - 【Conference on Empirical Methods in Natural Language Processing】
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification （2020.10.26）
Timo Schick, Helmut Schmid, Hinrich Schütze . - 【International Conference on Computational Linguistics】
