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Soft prompts have been popularized as a cheap and easy way to improve task-specific LLM performance beyond few-shot prompts.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts · 2013
Earlier work this paper cites.
Style transfer from non-parallel text by cross-alignment
T. Shen, T. Lei, R. Barzilay, and T. Jaakkola · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions
C. Clark, K. Lee, M.-W. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova · 2019
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh · 2020
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Cited alongside, same era.
Language-aware soft prompting for vision & language foundation models
A. Bulat and G. Tzimiropoulos · 2022
Cited alongside, same era.
Rlprompt: Optimizing discrete text prompts with reinforcement learning
M. Deng, J. Wang, C.-P. Hsieh, Y. Wang, H. Guo, T. Shu, M. Song, E. P. Xing, and Z. Hu · 2022
Later among the works it cites.
Benefits from variational regularization in language models
C. Ferner and S. Wegenkittl · 2022
Later among the works it cites.
Explaining patterns in data with language models via interpretable autoprompting
C. Singh, J. X. Morris, J. Aneja, A. M. Rush, and J. Gao · 2022
Later among the works it cites.
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Y. Wen, N. Jain, J. Kirchenbauer, M. Goldblum, J. Geiping, and T. Goldstein · 2023
Later among the works it cites.
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