# Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning

arXiv: https://arxiv.org/abs/2305.15212 · HTML: https://mlsanity.com/paper/2305.15212/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Reducing Transformer Depth on Demand with Structured Dropout](https://mlsanity.com/paper/1909.11556/) (2019)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [Parameter-Efficient Transfer Learning with Diff Pruning](https://mlsanity.com/paper/2012.07463/) (2020)
- [AdapterDrop: On the Efficiency of Adapters in Transformers](https://mlsanity.com/paper/2010.11918/) (2020)
- [Adaptable Adapters](https://mlsanity.com/paper/2205.01549/) (2022)

## Built on by (most cited first, 29 of 29)

- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies](https://mlsanity.com/paper/2410.19878/) (2024)
- [Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning](https://mlsanity.com/paper/2405.03279/) (2024)
- [Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models](https://mlsanity.com/paper/2407.01906/) (2024)
- [Position: Editing Large Language Models Poses Serious Safety Risks](https://mlsanity.com/paper/2502.02958/) (2025)
- [NeuSpeech: Decode Neural signal as Speech](https://mlsanity.com/paper/2403.01748/) (2024)
- [RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization](https://mlsanity.com/paper/2405.00657/) (2024)
- [MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba](https://mlsanity.com/paper/2411.03855/) (2024)
- [LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models](https://mlsanity.com/paper/2503.16843/) (2025)
- [UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter-Efficient Fine-Tuning of Large Models](https://mlsanity.com/paper/2505.20154/) (2025)
- [DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation](https://mlsanity.com/paper/2405.06368/) (2024)
- [An Overview of Low-Rank Structures in the Training and Adaptation of Large Models](https://mlsanity.com/paper/2503.19859/) (2025)
- [TS-SAM: Fine-Tuning Segment-Anything Model for Downstream Tasks](https://mlsanity.com/paper/2408.01835/) (2024)
- [Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision](https://mlsanity.com/paper/2411.01431/) (2024)
- [MoLEx: Mixture of Layer Experts for Finetuning with Sparse Upcycling](https://mlsanity.com/paper/2503.11144/) (2025)
- [Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy](https://mlsanity.com/paper/2409.07723/) (2024)
- [SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition](https://mlsanity.com/paper/2409.10429/) (2024)
- [Faster Parameter-Efficient Tuning with Token Redundancy Reduction](https://mlsanity.com/paper/2503.20282/) (2025)
- [DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying](https://mlsanity.com/paper/2405.13325/) (2024)
- [Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning](https://mlsanity.com/paper/2505.22355/) (2025)
- [Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper](https://mlsanity.com/paper/2311.13126/) (2023)
- [Noise Consistency Regularization for Improved Subject-Driven Image Synthesis](https://mlsanity.com/paper/2506.06483/) (2025)
- [LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning](https://mlsanity.com/paper/2507.20999/) (2025)
- [From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs](https://mlsanity.com/paper/2609.25655/) (2026)
- [ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation](https://mlsanity.com/paper/2405.04121/) (2024)
- [Leveraging Self-Attention for Input-Dependent Soft Prompting in LLMs](https://mlsanity.com/paper/2506.05629/) (2025)
- [A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models](https://mlsanity.com/paper/2508.02711/) (2025)
- [Dialect Identification Using Resource-Efficient Fine-Tuning Approaches](https://mlsanity.com/paper/2512.02074/) (2025)

## Abstract (arXiv)

Fine-tuning large pre-trained language models on various downstream tasks with whole parameters is prohibitively expensive. Hence, Parameter-efficient fine-tuning has attracted attention that only optimizes a few task-specific parameters with the frozen pre-trained model. In this work, we focus on prefix tuning, which only optimizes continuous prefix vectors (i.e. pseudo tokens) inserted into Transformer layers. Based on the observation that the learned syntax and semantics representation varies a lot at different layers, we argue that the adaptive prefix will be further tailored to each layer than the fixed one, enabling the fine-tuning more effective and efficient. Thus, we propose Adaptive Prefix Tuning (APT) to adjust the prefix in terms of both fine-grained token level and coarse-grained layer level with a gate mechanism. Experiments on the SuperGLUE and NER datasets show the effectiveness of APT. In addition, taking the gate as a probing, we validate the efficiency and effectiveness of the variable prefix.