Prefix Tuning

Prefix tuning is an LLM fine-tuning optimization that uses a vector of prefix tokens with values that are defined during fine-tuning. Hence, prefix tuning is similar to prompt tuning and other types of Parameter-Efficient Fine-Tuning (PEFT).

Research on Prefix Tuning

Research papers on prefix tuning include:

• 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 (Broad survey with many optimizations including this topic.)
• 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
• 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.)
• Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
• 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

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