# 📄 Soft Prompt

## Paper List

<div style="line-height:0.2em;">


[**Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness**](https://doi.org/10.48550/arXiv.2302.13793) （**2023.02.23**）

<font color="gray">G. Zuccon, B. Koopman .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-1-green)

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[**How Does In-Context Learning Help Prompt Tuning?**](https://doi.org/10.48550/arXiv.2302.11521) （**2023.02.22**）

<font color="gray">Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Scalable Prompt Generation for Semi-supervised Learning with Language Models**](https://doi.org/10.48550/arXiv.2302.09236) （**2023.02.18**）

<font color="gray">Yuhang Zhou, Suraj Maharjan, Bei Liu .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts**](https://doi.org/10.48550/arXiv.2302.08958) （**2023.02.17**）

<font color="gray">Zhihong Chen, Shizhe Diao, Benyou Wang, Guanbin Li, Xiang Wan .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)  [![](https://img.shields.io/badge/Github%20Stars-5-blue)](https://github.com/zhjohnchan/ptunifier)

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[**SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains**](https://doi.org/10.48550/arXiv.2302.06868) （**2023.02.14**）

<font color="gray">Koustava Goswami, Lukas Lange, J. Araki, Heike Adel .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)  [![](https://img.shields.io/badge/Github%20Stars-44-blue)](https://github.com/boschresearch/switchprompt)

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[**Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning**](https://doi.org/10.48550/arXiv.2301.10915) （**2023.01.26**）

<font color="gray">Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao, Arpit Gupta, Di Jin, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?**](https://doi.org/10.48550/arXiv.2212.10539) （**2022.12.20**）

<font color="gray">Weijia Shi, Xiaochuang Han, Hila Gonen, Ari Holtzman, Yulia Tsvetkov, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-1-green)

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[**Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI**](https://doi.org/10.48550/arXiv.2212.02924) （**2022.12.06**）

<font color="gray">Damith Chamalke Senadeera, Julia Ive .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning**](https://doi.org/10.48550/arXiv.2211.10681) （**2022.11.19**）

<font color="gray">Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)  [![](https://img.shields.io/badge/Github%20Stars-3-blue)](https://github.com/forest-art/dfsp)

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[**FPT: Improving Prompt Tuning Efficiency via Progressive Training**](https://doi.org/10.48550/arXiv.2211.06840) （**2022.11.13**）

<font color="gray">Yufei Huang, Yujia Qin, Huadong Wang, Yichun Yin, Maosong Sun, etc .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-0-green)  [![](https://img.shields.io/badge/Github%20Stars-8-blue)](https://github.com/thunlp/fastprompttuning)

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[**Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning**](https://doi.org/10.48550/arXiv.2210.12587) （**2022.10.23**）

<font color="gray">Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey, Chien-Sheng Wu, Caiming Xiong .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts**](https://doi.org/10.48550/arXiv.2210.11292) （**2022.10.20**）

<font color="gray">Xiangyang Liu, Tianxiang Sun, Xuanjing Huang, Xipeng Qiu .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-2-green)  [![](https://img.shields.io/badge/Github%20Stars-5-blue)](https://github.com/xyltt/lpt)

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[**Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models**](https://doi.org/10.48550/arXiv.2210.10841) （**2022.10.19**）

<font color="gray">Yue Zhang, Yueping Zhang, Hongliang Fei, Dingcheng Li, Tan Yu, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-2-green)

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[**XPrompt: Exploring the Extreme of Prompt Tuning**](https://doi.org/10.48550/arXiv.2210.04457) （**2022.10.10**）

<font color="gray">Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, etc .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-2-green)

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[**Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts**](https://doi.org/10.48550/arXiv.2210.04726) （**2022.10.10**）

<font color="gray">C. D. Santos, Zhe Dong, Daniel Matthew Cer, John Nham, Siamak Shakeri, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Retrieval of Soft Prompt Enhances Zero-Shot Task Generalization**](https://doi.org/10.48550/arXiv.2210.03029) （**2022.10.06**）

<font color="gray">Seonghyeon Ye, Joel Jang, Doyoung Kim, Yongrae Jo, Minjoon Seo .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-3-green)  [![](https://img.shields.io/badge/Github%20Stars-12-blue)](https://github.com/seonghyeonye/rospr)

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[**Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation**](https://doi.org/10.48550/arXiv.2210.02952) （**2022.10.06**）

<font color="gray">Xu Guo, Boyang Albert Li, Han Yu .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-3-green)  [![](https://img.shields.io/badge/Github%20Stars-14-blue)](https://github.com/liangcici/probes-vln)

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[**MetaPrompting: Learning to Learn Better Prompts**](https://doi.org/10.48550/arXiv.2209.11486) （**2022.09.23**）

<font color="gray">Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che .  - 【International Conference on Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-2-green)  [![](https://img.shields.io/badge/Github%20Stars-4-blue)](https://github.com/dousia/metaprompting)

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[**Prompt-based Conservation Learning for Multi-hop Question Answering**](https://doi.org/10.48550/arXiv.2209.06923) （**2022.09.14**）

<font color="gray">Zhenyun Deng, Yonghua Zhu, Yang Chen, Qianqian Qi, M. Witbrock, etc .  - 【International Conference on Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Prompt Tuning with Soft Context Sharing for Vision-Language Models**](https://doi.org/10.48550/arXiv.2208.13474) （**2022.08.29**）

<font color="gray">Kun Ding, Ying Wang, Pengzhang Liu, Qiang Yu, Hao Zhang, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-2-green)

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[**FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning**](https://arxiv.org/abs/2208.12268) （**2022.08.25**）

<font color="gray">Haodong Zhao, Wei Du, Fang Li, Peixuan Li, Gongshen Liu </font>

![](https://img.shields.io/badge/Citations-0-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-3-red)

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[**PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models - Federated Learning in Age of Foundation Model**](https://doi.org/10.48550/arXiv.2208.11625) （**2022.08.24**）

<font color="gray">Tao Guo, Song Guo, Junxiao Wang, Wenchao Xu .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-5-green)

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[**PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation**](https://doi.org/10.48550/arXiv.2208.10160) （**2022.08.22**）

<font color="gray">Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-11-green)

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[**No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence**](https://doi.org/10.1145/3540250.3549113) （**2022.07.24**）

<font color="gray">Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, etc .  - 【ESEC/SIGSOFT FSE】</font>

![](https://img.shields.io/badge/Citations-9-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-25-red)  [![](https://img.shields.io/badge/Github%20Stars-28-blue)](https://github.com/adf1178/pt4code)

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[**PTAU: Prompt Tuning for Attributing Unanswerable Questions**](https://doi.org/10.1145/3477495.3532048) （**2022.07.06**）

<font color="gray">Jinzhi Liao, Xiang Zhao, Jianming Zheng, Xinyi Li, Fei Cai, etc .  - 【Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval】</font>

![](https://img.shields.io/badge/Citations-0-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-6-red)

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[**Learning a Better Initialization for Soft Prompts via Meta-Learning**](https://doi.org/10.48550/arXiv.2205.12471) （**2022.05.25**）

<font color="gray">Yukun Huang, Kun Qian, Zhou Yu .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-2-green)

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[**ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts**](https://arxiv.org/abs/2205.11961) （**2022.05.24**）

<font color="gray">Akari Asai, Mohammadreza Salehi, Matthew E. Peters, Hannaneh Hajishirzi .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-4-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-15-red)  [![](https://img.shields.io/badge/Github%20Stars-58-blue)](https://github.com/akariasai/attempt)

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[**Structured Prompt Tuning**](https://doi.org/10.48550/arXiv.2205.12309) （**2022.05.24**）

<font color="gray">Chi-Liang Liu, Hung-yi Lee, Wen-tau Yih .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-1-green)  [![](https://img.shields.io/badge/Github%20Stars-6-blue)](https://github.com/zjukg/kgtransformer)

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[**Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding**](https://doi.org/10.48550/arXiv.2205.11024) （**2022.05.23**）

<font color="gray">Rishabh Bhardwaj, Amrita Saha, S. Hoi .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-0-green)  [![](https://img.shields.io/badge/Github%20Stars-3-blue)](https://github.com/declare-lab/vip)

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[**ProQA: Structural Prompt-based Pre-training for Unified Question Answering**](https://doi.org/10.48550/arXiv.2205.04040) （**2022.05.09**）

<font color="gray">Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, etc .  - 【North American Chapter of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-6-green)  [![](https://img.shields.io/badge/Github%20Stars-6-blue)](https://github.com/zhongwanjun/proqa)

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[**Prompt Distribution Learning**](https://doi.org/10.1109/CVPR52688.2022.00514) （**2022.05.06**）

<font color="gray">Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, Xinmei Tian .  - 【Computer Vision and Pattern Recognition】</font>

![](https://img.shields.io/badge/Citations-24-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-67-red)  [![](https://img.shields.io/badge/Github%20Stars-852-blue)](https://github.com/openai/following-instructions-human-feedback)

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[**HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification**](https://doi.org/10.48550/arXiv.2204.13413) （**2022.04.28**）

<font color="gray">Zihan Wang, Peiyi Wang, Tianyu Liu, Yunbo Cao, Zhifang Sui, etc .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-0-green)  [![](https://img.shields.io/badge/Github%20Stars-34-blue)](https://github.com/wzh9969/hpt)

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[**Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation**](https://doi.org/10.48550/arXiv.2204.13362) （**2022.04.28**）

<font color="gray">Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-9-green)

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[**PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization**](https://doi.org/10.48550/arXiv.2204.04413) （**2022.04.09**）

<font color="gray">Xiaochen Liu, Yu Bai, Jiawei Li, Yinan Hu, Yang Gao .  - 【International Conference on Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-7-green)

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[**Learning to Compose Soft Prompts for Compositional Zero-Shot Learning**](https://doi.org/10.48550/arXiv.2204.03574) （**2022.04.07**）

<font color="gray">Nihal V. Nayak, Peilin Yu, Stephen H. Bach .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-9-green)  [![](https://img.shields.io/badge/Github%20Stars-37-blue)](https://github.com/batsresearch/csp)

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[**Unsupervised Prompt Learning for Vision-Language Models**](https://doi.org/10.48550/arXiv.2204.03649) （**2022.04.07**）

<font color="gray">Hao Huang, Jack Chu, Fangyun Wei .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-17-green)  [![](https://img.shields.io/badge/Github%20Stars-67-blue)](https://github.com/tonyhuang2022/upl)

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[**Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning**](https://doi.org/10.1145/3539597.3570398) （**2022.04.01**）

<font color="gray">Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, etc .  - 【Web Search and Data Mining】</font>

![](https://img.shields.io/badge/Citations-3-green)  [![](https://img.shields.io/badge/Github%20Stars-1.5k-blue)](https://github.com/alibaba/EasyNLP/tree/master/examples/fewshot_learning)

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[**Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model**](https://doi.org/10.1109/CVPR52688.2022.01369) （**2022.03.28**）

<font color="gray">Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, etc .  - 【Computer Vision and Pattern Recognition】</font>

![](https://img.shields.io/badge/Citations-31-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-131-red)  [![](https://img.shields.io/badge/Github%20Stars-115-blue)](https://github.com/dyabel/detpro)

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[**Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning**](https://doi.org/10.48550/arXiv.2203.11720) （**2022.03.16**）

<font color="gray">Yuhui Zuo, Wei Zhu, Guoyong Cai .  - 【International Conference on Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-0-green)

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[**Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning**](https://arxiv.org/abs/2203.06875) （**2022.03.14**）

<font color="gray">Yu-Ying Jiang, Linhan Zhang, Wei Wang .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-0-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-11-red)  [![](https://img.shields.io/badge/Github%20Stars-101-blue)](https://github.com/yjiangcm/promcse)

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[**PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks**](https://doi.org/10.18653/v1/2022.acl-long.292) （**2022.02.25**）

<font color="gray">Yufei Wang, Can Xu, Qingfeng Sun, Huang Hu, Chongyang Tao, etc .  - 【Annual Meeting of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-17-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-97-red)  [![](https://img.shields.io/badge/Github%20Stars-47-blue)](https://github.com/garyyufei/promda)

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[**Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt**](https://arxiv.org/abs/2202.11451) （**2022.02.23**）

<font color="gray">Lianzhe Huang, Shuming Ma, Dongdong Zhang, Furu Wei, Houfeng Wang .  - 【Conference on Empirical Methods in Natural Language Processing】</font>

![](https://img.shields.io/badge/Citations-4-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-23-red)  [![](https://img.shields.io/badge/Github%20Stars-6-blue)](https://github.com/mojave-pku/uniprompt)

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[**Personalized Prompt Learning for Explainable Recommendation**](https://arxiv.org/abs/2202.07371) （**2022.02.15**）

<font color="gray">Lei Li, Yongfeng Zhang, Li Chen .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-10-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-13-red)  [![](https://img.shields.io/badge/Github%20Stars-57-blue)](https://github.com/lileipisces/pepler)

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[**Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey**](https://doi.org/10.1109/ACCESS.2022.3184801) （**2022.02.15**）

<font color="gray">Jiaqi Wen, B. Gabrys, Katarzyna Musial .  - 【IEEE Access】</font>

![](https://img.shields.io/badge/Citations-5-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-17-red)

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[**Context-Tuning: Learning Contextualized Prompts for Natural Language Generation**](https://arxiv.org/abs/2201.08670) （**2022.01.21**）

<font color="gray">Tianyi Tang, Junyi Li, Wayne Xin Zhao .  - 【International Conference on Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-9-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-51-red)  [![](https://img.shields.io/badge/Github%20Stars-8-blue)](https://github.com/rucaibox/context-tuning)

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[**Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer**](https://doi.org/10.1007/978-3-031-15931-2_19) （**2022.01.14**）

<font color="gray">Yinyi Wei, Tong Mo, Yong Jiang, Weiping Li, Wen Zhao .  - 【International Conference on Artificial Neural Networks】</font>

![](https://img.shields.io/badge/Citations-2-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-13-red)  [![](https://img.shields.io/badge/Github%20Stars-15-blue)](https://github.com/ydongd/prototypical-prompt-verbalizer)

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[**On Transferability of Prompt Tuning for Natural Language Processing**](https://doi.org/10.18653/v1/2022.naacl-main.290) （**2021.11.12**）

<font color="gray">Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, etc .  - 【North American Chapter of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-19-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-63-red)  [![](https://img.shields.io/badge/Github%20Stars-53-blue)](https://github.com/thunlp/Prompt-Transferability)

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[**OpenPrompt: An Open-source Framework for Prompt-learning**](https://doi.org/10.18653/v1/2022.acl-demo.10) （**2021.11.03**）

<font color="gray">Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, etc .  - 【Annual Meeting of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-59-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-139-red)  [![](https://img.shields.io/badge/Github%20Stars-2.6k-blue)](https://github.com/thunlp/OpenPrompt)

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[**SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer**](https://doi.org/10.18653/v1/2022.acl-long.346) （**2021.10.15**）

<font color="gray">Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, Daniel Matthew Cer .  - 【Annual Meeting of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-85-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-151-red)

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[**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks**](https://arxiv.org/abs/2110.07602) （**2021.10.14**）

<font color="gray">Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, etc .  - 【ArXiv】</font>

![](https://img.shields.io/badge/Citations-148-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-1-red)  [![](https://img.shields.io/badge/Github%20Stars-606-blue)](https://github.com/thudm/p-tuning-v2)

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[**PPT: Pre-trained Prompt Tuning for Few-shot Learning**](https://doi.org/10.18653/v1/2022.acl-long.576) （**2021.09.09**）

<font color="gray">Yuxian Gu, Xu Han, Zhiyuan Liu, Minlie Huang .  - 【Annual Meeting of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-107-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-276-red)  [![](https://img.shields.io/badge/Github%20Stars-66-blue)](https://github.com/thu-coai/ppt)

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[**Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning**](https://arxiv.org/abs/2106.09226) （**2021.06.17**）

<font color="gray">Colin Wei, Sang Michael Xie, Tengyu Ma .  - 【Neural Information Processing Systems】</font>

![](https://img.shields.io/badge/Citations-33-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-114-red)  [![](https://img.shields.io/badge/Github%20Stars-4-blue)](https://github.com/sangmichaelxie/pretraining_analysis)

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[**Learning How to Ask: Querying LMs with Mixtures of Soft Prompts**](https://doi.org/10.18653/V1/2021.NAACL-MAIN.410) （**2021.04.14**）

<font color="gray">Guanghui Qin, J. Eisner .  - 【North American Chapter of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-182-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-238-red)  [![](https://img.shields.io/badge/Github%20Stars-66-blue)](https://github.com/hiaoxui/soft-prompts)

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[**Transformers as Soft Reasoners over Language**](https://doi.org/10.24963/ijcai.2020/533) （**2020.02.14**）

<font color="gray">Peter Clark, Oyvind Tafjord, Kyle Richardson .  - 【International Joint Conference on Artificial Intelligence】</font>

![](https://img.shields.io/badge/Citations-158-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-91-red)  [![](https://img.shields.io/badge/Github%20Stars-27-blue)](https://github.com/allenai/ruletaker)

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[**P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks**](https://doi.org/10.18653/v1/2022.acl-short.8) 

<font color="gray">Xiao Liu, Kaixuan Ji, Yicheng Fu, W. Tam, Zhengxiao Du, etc .  - 【Annual Meeting of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-45-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-284-red)  [![](https://img.shields.io/badge/Github%20Stars-606-blue)](https://github.com/thudm/p-tuning-v2)

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[**Coherent Long Text Generation by Contrastive Soft Prompt**](https://api.semanticscholar.org/4dc3683fa223d160045bca575a8b5ecf94f61604) 



![](https://img.shields.io/badge/Citations-0-green)

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[**Continuous Prompt Tuning for Russian: How to Learn Prompts Efficiently with RuGPT3?**](https://doi.org/10.1007/978-3-031-15168-2_3) 

<font color="gray">Nikita Konodyuk, M. Tikhonova .  - 【International Joint Conference on the Analysis of Images, Social Networks and Texts】</font>

![](https://img.shields.io/badge/Citations-3-green)

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[**PPT: Backdoor Attacks on Pre-trained Models via Poisoned Prompt Tuning**](https://api.semanticscholar.org/b5da0ffa7b60abefa036bc450e8e333087943787) 



![](https://img.shields.io/badge/Citations-0-green)

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[**PTAU: Prompt Tuning for Attributing Unanswerable Questions**](https://api.semanticscholar.org/97d214a10afb84f3b94881a3ad8e90372ba5809c) 



![](https://img.shields.io/badge/Citations-0-green)

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[**Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis**](https://doi.org/10.18653/v1/2022.acl-long.174) 

<font color="gray">Hui-Hsin Wu, Xiaodon Shi .  - 【Annual Meeting of the Association for Computational Linguistics】</font>

![](https://img.shields.io/badge/Citations-5-green)  ![](https://img.shields.io/badge/Mendeley%20Readers-52-red)

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[**Changing the Narrative Perspective: From Ranking to Prompt-Based Generation of Entity Mentions**](https://api.semanticscholar.org/b70363498cb912b31c4647f92e848047f01c2754) 



![](https://img.shields.io/badge/Citations-0-green)

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[**Enhance Performance of Ad-hoc Search via Prompt Learning**](https://doi.org/10.1007/978-3-031-24755-2_3) 

<font color="gray">Shenghao Yang, Yiqun Liu, Xiaohui Xie, M. Zhang, Shaoping Ma .  - 【China Conference on Information Retrieval】</font>

![](https://img.shields.io/badge/Citations-0-green)


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# CONTINUE...

