Collection of Tools and Papers related to Adapters (aka Parameter-Efficient Transfer Learning/ Fine-Tuning)
This repository collects important tools and papers related to adapter methods for recent large pre-trained neural networks.
Adapters (aka Parameter-Efficient Transfer Learning (PETL) or Parameter-Efficient Fine-Tuning (PEFT) methods) include various parameter-efficient approaches of adapting large pre-trained models to new tasks.
Large pre-trained (Transformer-based) models have become the foundation of various ML domains in recent years. While the most prevalent method of adapting these models to new tasks involves costly full fine-tuning of all model parameters, a series of parameter-efficient and lightweight alternatives, adapters, have been established in recent time.
Using adapters provides multiple benefits. They are ...
AdapterHub: A Framework for Adapting Transformers
Conference on Empirical Methods in Natural Language Processing
Jonas Pfeiffer, Andreas Rücklé, Clifton A. Poth, Aishwarya Kamath, Ivan Vulic, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych (2020)
OpenDelta
PEFT: State-of-the-art Parameter-Efficient Fine-Tuning
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
arXiv.org
Zhiqiang Hu, Yihuai Lan, Lei Wang, Wanyu Xu, Ee-Peng Lim, R. Lee, Lidong Bing, Soujanya Poria (2023)
Alpaca-LoRA
Modular Deep Learning
arXiv.org
Jonas Pfeiffer, Sebastian Ruder, Ivan Vulic, E. Ponti (2023)
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
arXiv.org
Vladislav Lialin, Vijeta Deshpande, Anna Rumshisky (2023)
PEFT-Ref: A Modular Reference Architecture and Typology for Parameter-Efficient Finetuning Techniques
arXiv.org
Mohammed Sabry, Anya Belz (2023)
Parameter-Efficient Transfer Learning for NLP
International Conference on Machine Learning
N. Houlsby, A. Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, S. Gelly (2019)
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Conference on Empirical Methods in Natural Language Processing
Jonas Pfeiffer, Ivan Vulic, Iryna Gurevych, Sebastian Ruder (2020)
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Conference of the European Chapter of the Association for Computational Linguistics
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, Iryna Gurevych (2020)
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Findings
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou (2020)
Parameter-Efficient Transfer Learning with Diff Pruning
Annual Meeting of the Association for Computational Linguistics
Demi Guo, Alexander M. Rush, Yoon Kim (2020)
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Annual Meeting of the Association for Computational Linguistics
Xiang Lisa Li, Percy Liang (2021)
The Power of Scale for Parameter-Efficient Prompt Tuning
Conference on Empirical Methods in Natural Language Processing
Brian Lester, Rami Al-Rfou, Noah Constant (2021)
Towards a Unified View of Parameter-Efficient Transfer Learning
International Conference on Learning Representations
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig (2021)
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
Neural Information Processing Systems
Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder (2021)
LoRA: Low-Rank Adaptation of Large Language Models
International Conference on Learning Representations
J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Weizhu Chen (2021)
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
Annual Meeting of the Association for Computational Linguistics
Rabeeh Karimi Mahabadi, Sebastian Ruder, M. Dehghani, J. Henderson (2021)
MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
Conference on Empirical Methods in Natural Language Processing
Alan Ansell, E. Ponti, Jonas Pfeiffer, Sebastian Ruder, Goran Glavas, Ivan Vulic, A. Korhonen (2021)
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
Annual Meeting of the Association for Computational Linguistics
Elad Ben-Zaken, Shauli Ravfogel, Yoav Goldberg (2021)
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Neural Information Processing Systems
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel (2022)
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
arXiv.org
Han Zhou, Xingchen Wan, Ivan Vulic, A. Korhonen (2023)
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
arXiv.org
Qingru Zhang, Minshuo Chen, Alexander W. Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao (2023)
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Workshop on Knowledge Extraction and Integration for Deep Learning Architectures; Deep Learning Inside Out
Anne Lauscher, Olga Majewska, Leonardo F. R. Ribeiro, Iryna Gurevych, N. Rozanov, Goran Glavavs (2020)
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
Annual Meeting of the Association for Computational Linguistics
Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, Luo Si (2021)
Robust Transfer Learning with Pretrained Language Models through Adapters
Annual Meeting of the Association for Computational Linguistics
Wenjuan Han, Bo Pang, Y. Wu (2021)
AdapterDrop: On the Efficiency of Adapters in Transformers
Conference on Empirical Methods in Natural Language Processing
Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych (2020)
What to Pre-Train on? Efficient Intermediate Task Selection
Conference on Empirical Methods in Natural Language Processing
Clifton A. Poth, Jonas Pfeiffer, Andreas Ruckl'e, Iryna Gurevych (2021)
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer
arXiv.org
M. Vidoni, Ivan Vulic, Goran Glavas (2020)
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks
Annual Meeting of the Association for Computational Linguistics
Xiao Liu, Kaixuan Ji, Yicheng Fu, W. Tam, Zhengxiao Du, Zhilin Yang, Jie Tang (2022)
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
arXiv.org
Ning Ding, Yujia Qin, Guang Yang, Fu Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Haitao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juan Li, Maosong Sun (2022)
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning
Annual Meeting of the Association for Computational Linguistics
Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Wen-tau Yih, Madian Khabsa (2021)
Simple, Scalable Adaptation for Neural Machine Translation
Conference on Empirical Methods in Natural Language Processing
Ankur Bapna, N. Arivazhagan, Orhan Firat (2019)
Monolingual Adapters for Zero-Shot Neural Machine Translation
Conference on Empirical Methods in Natural Language Processing
Jerin Philip, Alexandre Bérard, Matthias Gallé, L. Besacier (2020)
UDapter: Language Adaptation for Truly Universal Dependency Parsing
Conference on Empirical Methods in Natural Language Processing
A. Ustun, Arianna Bisazza, G. Bouma, Gertjan van Noord (2020)
Single-dataset Experts for Multi-dataset Question Answering
Conference on Empirical Methods in Natural Language Processing
Dan Friedman, Ben Dodge, Danqi Chen (2021)
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
Conference on Empirical Methods in Natural Language Processing
Jonas Pfeiffer, Ivan Vulic, Iryna Gurevych, Sebastian Ruder (2020)
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters
Conference on Machine Translation
Asa Cooper Stickland, Alexandre Berard, Vassilina Nikoulina (2021)
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters
Conference on Empirical Methods in Natural Language Processing
A. Ustun, Alexandre Berard, L. Besacier, Matthias Gallé (2021)
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
Conference on Empirical Methods in Natural Language Processing
Xinyi Wang, Yulia Tsvetkov, Sebastian Ruder, Graham Neubig (2021)
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
arXiv.org
Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, Y. Qiao (2023)
Learning multiple visual domains with residual adapters
NIPS
Sylvestre-Alvise Rebuffi, Hakan Bilen, A. Vedaldi (2017)
Efficient Parametrization of Multi-domain Deep Neural Networks
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Sylvestre-Alvise Rebuffi, Hakan Bilen, A. Vedaldi (2018)
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
arXiv.org
Hao Chen, R. Tao, Han Zhang, Yidong Wang, Weirong Ye, Jindong Wang, Guosheng Hu, M. Savvides (2022)
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Neural Information Processing Systems
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, Ping Luo (2022)
Lightweight Adapter Tuning for Multilingual Speech Translation
Annual Meeting of the Association for Computational Linguistics
Hang Le, J. Pino, Changhan Wang, Jiatao Gu, D. Schwab, L. Besacier (2021)
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition
IEEE International Conference on Acoustics, Speech, and Signal Processing
Bethan Thomas, Samuel Kessler, S. Karout (2022)
VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
Computer Vision and Pattern Recognition
Yi-Lin Sung, Jaemin Cho, Mohit Bansal (2021)
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
Neural Information Processing Systems
Yi-Lin Sung, Jaemin Cho, Mohit Bansal (2022)
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
arXiv.org
Tao Lei, Junwen Bai, Siddhartha Brahma, J. Ainslie, Kenton Lee, Yanqi Zhou, Nan Du, Vincent Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang (2023)
Contributions of new awesome adapter-related resources are very welcome! Before contributing, make sure to read this repository's contributing guide.
Paper metadata is partially retrieved via Semantic Scholar's API. Paper TLDRs are provided by Semantic Scholar's TLDR feature.