Prompt Tuning

Prompt tuning is an LLM optimization that adds special "prompt tokens" to the input sequence. The goals of prompt tuning are similar to fine-tuning, but with improved efficiency compared with adjusting all the model parameters. Thus it is analogous to Parameter-Efficent Fine-Tuning (PEFT), including LoRA, but it works in a completely different way.

Note that the term "prompt tuning" is sometimes used in its general meaning, which means tuning of prompts. In this case, it is referring to automatic prompt optimization, or "programmatic prompting."

Research on Prompt Tuning

• IBM, 2024, What is prompt-tuning?, https://research.ibm.com/blog/what-is-ai-prompt-tuning
• Abhinav Jain, Swarat Chaudhuri, Thomas Reps, Chris Jermaine, 24 May 2024, Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation, https://arxiv.org/abs/2405.15282
• MohammadAli SadraeiJavaeri, Ehsaneddin Asgari, Alice Carolyn McHardy, Hamid Reza Rabiee, 7 Jun 2024, SuperPos-Prompt: Enhancing Soft Prompt Tuning of Language Models with Superposition of Multi Token Embeddings, https://arxiv.org/abs/2406.05279
• Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella, 5 Jun 2024, Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need, https://arxiv.org/abs/2406.03216
• Xuyang Wu, Zhiyuan Peng, Sravanthi Rajanala, Hsin-Tai Wu, Yi Fang, 31 May 2024, Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models, https://arxiv.org/abs/2405.20654
• Wei Zhu, Aaron Xuxiang Tian, Congrui Yin, Yuan Ni, Xiaoling Wang, Guotong Xie, 7 Jun 2024 (v2), IAPT: Instruction-Aware Prompt Tuning for Large Language Models, https://arxiv.org/abs/2405.18203
• Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
• 18 Apr 2024 (v2), The Efficiency Spectrum of Large Language Models: An Algorithmic Survey, Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang, https://arxiv.org/abs/2312.00678
• M Xu, D Cai, W Yin, S Wang, X Jin, X Liu - ACM Computing Surveys, 2024, Resource-efficient Algorithms and Systems of Foundation Models: A Survey, https://dl.acm.org/doi/pdf/10.1145/3706418
• Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. https://aclanthology.org/2021.emnlp-main.243/
• Shreyansh Shah, Oct 18, 2023, Prompt Tuning: A Powerful Technique for Adapting LLMs to New Tasks, https://medium.com/@shahshreyansh20/prompt-tuning-a-powerful-technique-for-adapting-llms-to-new-tasks-6d6fd9b83557
• Data Camp, May 19, 2024, Understanding Prompt Tuning: Enhance Your Language Models with Precision, https://www.datacamp.com/tutorial/understanding-prompt-tuning
• Sergey Sedov, Sumanth Bharadwaj Hachalli Karanam, Venu Gopal Kadamba, 24 Dec 2024, Exploring Embedding Priors in Prompt-Tuning for Improved Interpretability and Control, https://arxiv.org/abs/2412.18582
• Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, Jie Tang, 20 Mar 2022 (v3), P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks, Proceedings of the 60th Annual Meeting of the Association of Computational Linguistics, 2022, https://arxiv.org/abs/2110.07602 https://github.com/THUDM/P-tuning-v2 (Extends prompt tuning with extra soft prompt tokens at every layer, not just at the start of the input.)
• Haowei Zhu, Fangyuan Zhang, Rui Qin, Tianxiang Pan, Junhai Yong, Bin Wang, 24 Dec 2024 (v2), Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning, https://arxiv.org/abs/2412.16956
• Xiang Lisa Li, Percy Liang, 1 Jan 2021, Prefix-Tuning: Optimizing Continuous Prompts for Generation, https://arxiv.org/abs/2101.00190 (Precursor to prompt tuning.)
• Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
• Qi Sun, Edoardo Cetin, Yujin Tang, 14 Jan 2025 (v2), Transformer2: Self-adaptive LLMs, https://arxiv.org/abs/2501.06252 (Using a vector to fine-tuning dynamically.)
• Liu Yang, Ziqian Lin, Kangwook Lee, Dimitris Papailiopoulos, Robert Nowak, 16 Jan 2025, Task Vectors in In-Context Learning: Emergence, Formation, and Benefit, https://arxiv.org/abs/2501.09240
• Dan Zhang, Tao Feng, Lilong Xue, Yuandong Wang, Yuxiao Dong, Jie Tang, 23 Jan 2025, Parameter-Efficient Fine-Tuning for Foundation Models, https://arxiv.org/abs/2501.13787
• X Li, C Jiang, Nov 2024, Optimizing Prompt Engineering Methods for Enhanced Logical Reasoning in Transformer Models, RMEL ’24, November 4–7, 2024, Hangzhou, China, https://www.researchgate.net/profile/Xiaoyan-Li-42/publication/389182048_Optimizing_Prompt_Engineering_Methods_for_Enhanced_Logical_Reasoning_in_Transformer_Models/links/67b82fa9461fb56424e3fc72/Optimizing-Prompt-Engineering-Methods-for-Enhanced-Logical-Reasoning-in-Transformer-Models.pdf https://github.com/xiaoyanLi629/RMELS2024

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