Parameter-Efficient Transfer Learning with Diff Pruning

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929

Distributed Representations of Sentences and DocumentsPreprint

Le, Q. V., & Mikolov, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.4053 · Cited by 7,905

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899

HEIRS: Hybrid Three-Dimension RRAM- and SRAM-CIM Architecture for Multi-task Transformer Acceleration

Xu, L., Yuan, S., Wang, D., Chen, Y., Li, X., & Sun, Y. (2024) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3649329.3657327 · Cited by 10

Efficient Fine-Tuning of Large Language Models via a Low-Rank Gradient Estimator

Zhang, L., Lou, Z., Ying, Y., Yang, C., & Zhou, H. (2024) · Applied Sciences · doi:10.3390/app15010082 · Cited by 9

Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation

Alzubi, T. M., & Mukhtar, H. (2025) · IEEE Access · doi:10.1109/access.2025.3569073 · Cited by 9

Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning

Tian, Z., Liu, Y., & Sun, Q. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02145 · Cited by 10

Intrinsic Gradient Compression for Scalable and Efficient Federated Learning

Melas-Kyriazi, L., & Wang, F. (2022) · doi:10.18653/v1/2022.fl4nlp-1.4 · Cited by 9

Parameter-Efficient Tuning with Special Token Adaptation

Yang, X., Huang, J. Y., Zhou, W., & Chen, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.60 · Cited by 13

DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model

Gao, C., & Zhang, S. Q. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.802 · Cited by 24

Learned Adapters Are Better Than Manually Designed Adapters

Zhang, Y., Wang, P., Tan, M., & Zhu, W. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.468 · Cited by 9

Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding

Shimomoto, E. K., Marrese-Taylor, E., Takamura, H., Kobayashi, I., Nakayama, H., & Miyao, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.829 · Cited by 10

Effective Continual Learning for Text Classification with Lightweight Snapshots

Wang, J., Dong, D., Shou, L., Chen, K., & Chen, G. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26206 · Cited by 8

Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

Feng, J., Li, Y., Jiang, C., Liu, Y., Li, M., & Hu, Q. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.1068165 · Cited by 9

InA: Inhibition Adaption on pre-trained language models

Kang, C., Prokop, J., Tong, L., Zhou, H., Hu, Y. S., & Novák, D. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106410 · Cited by 9

Domain-Conditioned Transformer for Fully Test-time Adaptation

Tang, Y., Chen, S., Jia, J., Zhang, Y. H., & He, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680678 · Cited by 8

Does Combining Parameter‐Efficient Modules Improve Few‐Shot Transfer Accuracy?

Asadi, N., Beitollahi, M., Khalil, Y. H., Li, Y., Zhang, G., & Chen, X. (2025) · Artificial Intelligence for Engineering · doi:10.1049/aie2.12002 · Cited by 14

TADA: Efficient Task-Agnostic Domain Adaptation for Transformers

Hung, C.-C., Lange, L., & Strötgen, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.31 · Cited by 9

Parameter-Efficient Tuning Makes a Good Classification Head

Yang, Z., Ding, M., Guo, Y., Lv, Q., & Tang, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.514 · Cited by 11

TR-Adapter: Parameter-Efficient Transfer Learning for Video Question Answering

Wang, Y., Liu, M., Song, X., & Nie, L. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521708 · Cited by 11

Exploring the Impact of Model Scaling on Parameter-Efficient Tuning

Su, Y., Chan, C.-M., Cheng, J., Qin, Y., Lin, Y., Hu, S., Yang, Z., Ding, N., Sun, X., Xie, G., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.931 · Cited by 8

MoIL: Momentum Imitation Learning for Efficient Vision-Language Adaptation

Luo, G., Zhou, Y., Huang, M., Ren, T., Sun, X., & Ji, R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3435790 · Cited by 10

SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models

Chen, X., Liu, J., Wang, Y., Wang, P. P., Brand, M., Wang, G., & Koike–Akino, T. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00804 · Cited by 8

PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation

Benedek, N., & Wolf, L. (2024) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2024.findings-eacl.18 · Cited by 12

DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

Chen, X., Chen, T., Chen, W., Awadallah, A. H., Wang, Z., & Cheng, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.456 · Cited by 15

Modular and Parameter-Efficient Fine-Tuning for NLP Models

Ruder, S., Pfeiffer, J., & Vulić, I. (2022) · doi:10.18653/v1/2022.emnlp-tutorials.5 · Cited by 7

Sam-Based Instance Segmentation Models for the Automation of Structural Damage DetectionPreprint

Ye, Z., Lovell, L., Faramarzi, A., & Ninić, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4750668 · Cited by 7

Petals: Collaborative Inference and Fine-tuning of Large Models

Borzunov, A., Baranchuk, D., Dettmers, T., Ryabinin, M., Belkada, Y., Chumachenko, A., Samygin, P., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2209.01188 · Cited by 6

Content-Adaptive Optimization Framework for Universal Deep Image Compression

Tsubota, K., & Aizawa, K. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023edp7114 · Cited by 7

A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models

Afrin, S., Haque, M. Z., & Mastropaolo, A. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3796522 · Cited by 12

Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment

Zhang, Z., Wang, J., & Wang, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.483 · Cited by 7

Dropout Mixture Low-Rank Adaptation for Visual Parameters-Efficient Fine-Tuning

Fang, Z., Wang, Y., Yi, R., & Ma, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72667-5_21 · Cited by 7

SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning

Diao, H., Wan, B., Jia, X., Zhuge, Y., Zhang, Y., Lu, H., & Chen, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72784-9_5 · Cited by 8

Lingual-Fusion Adapter-Based Transfer Learning for Low-Resource Code Vulnerability Detection

Long, X., Guo, S., Chai, Y., Li, H., Jan, S. A., Ma, Q., & Qiao, N. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3535638 · Cited by 7

Parameter-efficient fine-tuning with layer pruning on medical sequence-to-sequence modeling

Zhu, Y., Wu, Y., Zhang, W., & Yang, X. (2026) · Health Information Science and Systems · doi:10.1007/s13755-026-00453-y · Cited by 9

Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU

Cao, J. P., Prakash, C. S., & Hamza, W. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.64 · Cited by 7

Image Recognition Algorithm Based on Improved AlexNet and Shared Parameter Transfer Learning

Jin Lu1, X. W. (2022) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2022.051202 · Cited by 6

Cross-Lingual Transfer with Target Language-Ready Task Adapters

Parović, M., Ansell, A., Vulić, I., & Korhonen, A. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.13 · Cited by 10

A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER

Zhang, Y., & Wang, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.135 · Cited by 6

LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models

Cai, X., Pan, R., & Yang, H. S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01385 · Cited by 6

Flexible and scalable federated learning with deep feature prompts for digital pathology

Cong, C., Song, Y., Ieva, A. D., Chou, A. S. Y., Gill, A. J., Coiera, E. W., & Liu, S. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02710-6 · Cited by 5

Step-by-Step Unmasking for Parameter-Efficient Fine-Tuning of Large Language Models

Agarwal, A., Ramesh, S. K., Sengupta, A., & Chakraborty, T. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.59 · Cited by 6

Detecting Source Code Vulnerabilities Using Fine-Tuned Pre-Trained LLMs

Zhu, J., Ge, H., Zhou, Y., Jin, X., Luo, R., & Sun, Y. (2024) · International Conference on Signal Processing · doi:10.1109/icsp62129.2024.10846595 · Cited by 5

Practical Offloading for Fine-Tuning LLM on Commodity GPU via Learned Sparse Projectors

Chen, S., Wang, Z., Guan, Z., Liu, Y., & Gibbons, P. B. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i22.34531 · Cited by 7

Split Learning Based Cloud-Edge-End Collaborative Model Training in Heterogeneous Networks

Wang, J., Feng, G., Liu, Y.-J., Xu, X., Cheng, L., Jiang, W., & Qian, L. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3597161 · Cited by 6

PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers

Zhong, Y., Jiang, H., Li, L., Nakada, R., Liu, T., Zhang, L., Yao, H., & Wang, H. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3780230 · Cited by 16

Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN

Shin, J., Choi, S., Ra, J., & Kim, L. (2022) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3489517.3530455 · Cited by 6

Adaptive Fine-Tuning in Degradation-Time-Series Forecasting via Generating Source Domain

Pan, J., Jin, B., Wang, S., Yuwen, X., & Jiao, X. (2023) · IEEE Access · doi:10.1109/access.2023.3341159 · Cited by 5

Decomposed Prompt Tuning via Low-Rank Reparameterization

Xiao, Y., Xu, L., Li, J., Lu, W., & Li, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.890 · Cited by 8

AI Act for the Working Programmer

Hermanns, H., Lauber-Rönsberg, A., Meinel, P., Sterz, S., & Zhang, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75434-0_6 · Cited by 7

Efficiently Tuned Parameters Are Task Embeddings

Zhou, W., Xu, C., & McAuley, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.334 · Cited by 8

DueT: Image-Text Contrastive Transfer Learning with Dual-adapter Tuning

Hasegawa, T., Nishida, K., Maeda, K., & Saito, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.839 · Cited by 5

One is Not Enough: Parameter-Efficient Fine-Tuning With Multiplicative Sparse Factorization

Chen, X., Chen, T., Cheng, Y. J., Chen, W., Awadallah, A. H., & Wang, Z. (2024) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2024.3431927 · Cited by 4

Efficient Computation Sharing for Multi-Task Visual Scene Understanding

Shoouri, S., Yang, M., Fan, Z., & Kim, H.-S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01571 · Cited by 6

UniTrans: Unified Parameter-Efficient Transfer Learning and Multimodal Alignment for Large Multimodal Foundation Model

Sun, J., Chen, K., He, X., Liu, X., Li, K., & Cheng, P. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.059745 · Cited by 4

Empirical studies of parameter efficient methods for large language models of code and knowledge transfer to R

Esmaeili, A., Saberi, I., & Fard, F. H. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10740-z · Cited by 6

The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

Mei, J., Yang, Y., Gao, Z., Rui, L., & Lin, Y. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.051860 · Cited by 4

LONG EXPOSURE: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity

Wang, T., Li, K., Hao, Z., Bai, D., Ren, J., Zhang, Y., Cao, T., & Yang, M. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00081 · Cited by 6

MGen: A Framework for Energy-Efficient In-ReRAM Acceleration of Multi-Task BERT

Kang, M., Shin, H., Kim, J., & Kim, L. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3288749 · Cited by 4

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning

Xie, S., Qiu, J., Pasad, A., Du, L., Qu, Q., & Mei, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.422 · Cited by 12

Controlling the Focus of Pretrained Language Generation Models

Ji, J., Kim, Y., Glass, J. R., & He, T. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.260 · Cited by 4

UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation

Chen, Y., Du, C., Li, C., Hu, J., Shi, Y., Xiong, S., Zhu, X. X., & Mou, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73471-7_12 · Cited by 3

Multi-TuneV: Fine-tuning the fusion of multiple modules for video action recognition

Liu, X., Ye, J., Wang, J., Xu, G., Li, Y., & Zheng, C. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104441 · Cited by 3

ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning

Hao, Z., Guo, J., Shen, L., Luo, Y., Hu, H., & Wen, Y. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02440-4 · Cited by 5

LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning

Shi, Y., Wu, Y., Wei, J., Ran, R., Sun, C., He, S., & Yang, Y. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3655172 · Cited by 9

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices

Wang, Y., He, J., & Yu, S. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423250 · Cited by 4

Lightweight Inference by Neural Network Pruning: Accuracy, Time and Comparison

Paralikas, I., Spantideas, S., Giannopoulos, A., & Trakadas, P. T. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63219-8_19 · Cited by 3

CoTuning: A Large-Small Model Collaborating Distillation Framework for Better Model Generalization

Liu, Z., Liu, K., Guo, M., Zhang, S. S., & Wang, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681462 · Cited by 3

WTConv-Adapter: wavelet transform convolution adapter for parameter efficient transfer learning of object detection ConvNets

Jia, G., Xu, L., & Zhu, L. (2025) · International Conference on Video and Image Processing (ICVIP) · doi:10.1117/12.3059115 · Cited by 3

Accelerating Diffusion Transformer via Error-Optimized Cache

Qiu, J., Wang, S., Lu, J., Liu, L., Jiang, H., Zhu, X., & Hao, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755009 · Cited by 9

Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models

Na, C., Mehta, S. V., & Strubell, E. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.361 · Cited by 14

On the instability of further pre-training: Does a single sentence matter to BERT?

Bacco, L., Minnema, G., Caselli, T., Dell’Orletta⋄, F., Merone, M., & Nissim, M. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100037 · Cited by 3

Effective Fine-tuning Method for Tibetan Low-resource Dialect Speech Recognition

Yang, J., Wei, J., Khysru, K., Xu, J., Lu, W., Ke, W., & Yang, X. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317230 · Cited by 3

Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity

Ansell, A., Parović, M., Vulić, I., Korhonen, A., & Ponti, E. M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.242 · Cited by 3

Adapting Open-Source LLMs for Contract Drafting and Analyzing Multi-Role vs. Single-Role Behavior of ChatGPT for Synthetic Data Generation

Kasundra, J., & Dhankhar, S. (2023) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3639856.3639888 · Cited by 3

ERAT-DLoRA: Parameter-efficient tuning with enhanced range adaptation in time and depth aware dynamic LoRA

Dan, L., Zheng, K., Wu, C., Wang, X., & Wang, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128778 · Cited by 4

FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images

Yu, J., Nie, Y., Qi, F., Liao, W., & Cai, H. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822454 · Cited by 3

DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment

Jiang, Z., Chen, T., Chen, X., Cheng, Y., Zhou, L., Yuan, L., Awadallah, A. H., & Wang, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_14 · Cited by 4

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation-Nearest Neighbor Neural Machine Translation

Cao, Z., Yang, B., Lin, H., Wu, S., Wei, X., Liu, D., Xie, J., Zhang, M., & Su, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.321 · Cited by 3

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

(2022) · doi:10.18653/v1/2022.emnlp-tutorials · Cited by 3

Embedding Hallucination for Few-shot Language Fine-tuning

Jian, Y., Gao, C., & Vosoughi, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.404 · Cited by 3

Refining the Giants: A Comprehensive Review of Fine-Tuning Strategies for Large Language Models

Singh, K., Deshpande, S., & Patwardhan, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-7839-3_5 · Cited by 3

STP: Special token prompt for parameter-efficient tuning of pre-trained language models

Yan, Y., Yu, H., Wang, D., Ye, J., Liu, F., & Xu, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127665 · Cited by 4

Low-Rank Prompt Adaptation for Open-Vocabulary Object Detection

Zhang, Z., Truong, V. Q., & Hoai, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00443 · Cited by 2

The BSRA framework for dual sparse parameter efficient fine tuning with block structured gating and rank adaptation

Jian, W., Chen, G., Hong, H., Zhang, Y., Du, C., & Bao, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-40835-6 · Cited by 2

Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System

Zhu, T., Zhao, Z., Liu, W., Liu, J., Chen, Y., Mao, W., Liu, H., Ding, K., Li, Y., & Yang, X. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-industry.35 · Cited by 2

Low-Rank Updates of pre-trained Weights for Multi-Task Learning

Audibert, A. D., Amini, M.-R., Usevich, K., & Clausel, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.476 · Cited by 3

Easy Pruning via Coresets and Structural Re-Parameterization

Yin, W., Dong, G., An, D., Zhao, Y., & Wang, B. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3419419 · Cited by 2

Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models

Zhu, M., Zhong, Q., Shen, L., Ding, L., Liu, J., Du, B., & Tao, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.696 · Cited by 3

MSLoRA: Meta-learned scaling for adaptive fine-tuning of LoRA

Dan, L., Zheng, K., Wu, C., & Wang, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130374 · Cited by 2

Parameter-Efficient Fine-Tuning via Circular Convolution

Chen, A., Cheng, J., Liu, Z., Gao, Z., Tsung, F., Li, Y., & Li, J. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.102 · Cited by 6

Attribute Controlled Dialogue Prompting

Liu, R., Rashid, A., Kobyzev, I., Rezagholizadeh, M., & Poupart, P. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.150 · Cited by 2

Ymir: A Scheduler for Foundation Model Fine-tuning Workloads in Datacenters

Gao, W., Zhuang, W., Li, M., Sun, P., Wen, Y., & Zhang, T. (2024) · International Conference on Supercomputing · doi:10.1145/3650200.3656599 · Cited by 3

A pruning-then-quantization model compression framework for facial emotion recognition

Sun, H., Shao, W., Li, T., Zhao, J., Xu, W., & Song, L. (2023) · Intelligent and Converged Networks · doi:10.23919/icn.2023.0020 · Cited by 1

Adapting CLIP for Action Recognition via Dual Semantic Supervision and Temporal Prompt Reparameterization

Deng, L., Tan, J., & Liu, F. (2024) · Electronics · doi:10.3390/electronics13163348 · Cited by 1

Adaptive Multiple-Attribute Scenario LoRA Merge for Robust Perception in Autonomous Driving

Kawata, R., Lee, J., Gu, Y., & Kamijo, S. (2026) · Sensors · doi:10.3390/s26041336 · Cited by 2

VL-MPFT: Multitask Parameter-Efficient Fine-Tuning for Visual-Language Pre-trained Models via Task-Adaptive Masking

Zhu, M., Liu, G., & Wei, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8620-6_26 · Cited by 1

Task-Agnostic Adaptive Activation Scaling Network for LLMs

Jia, N., Liu, T., Chen, J. C., Zhang, Y., & Han, S. (2025) · IEEE Access · doi:10.1109/access.2025.3542415 · Cited by 1

A Bayesian Interpretation of Adaptive Low-Rank Adaptation

Chen, H., & Garner, P. N. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890798 · Cited by 3

Bridging Spatial and Temporal Contexts: Sparse Transfer Learning

Persson, D., Wahlberg, W., Vettoruzzo, A., & Nowaczyk, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91398-3_25 · Cited by 1

Progtuning: Progressive Fine-Tuning Framework for Transformer-Based Language Models

Ji, X., Zhao, Z., Chen, X., Zhao, X., & Liu, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6599-0_21 · Cited by 1

RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates

Kowsher, M., Esmaeilbeig, T., Yu, C.-N., Chen, C., Soltanalian, M., & Yousefi, N. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1293 · Cited by 10

Multimedia Event Extraction with LLM Knowledge Editing

Yu, J., Lin, Y., Gao, Z., Qiu, X., & Rui, L. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.205 · Cited by 2

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer

Du, G., Fang, Z., Li, J., Li, J., Jiang, R., Yu, S., Guo, Y., Chen, Y., Goh, S. K., Tang, H.-K., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1570 · Cited by 4

DimA: A Parameter-efficient Fine-tuning Method with Knowledge Transfer Based on Transformer

Zhang, W., Huang, M., Song, Z., & Miao, Q. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/34asv3xtjq9h

Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning

Poth, C., Sterz, H., Paul, I., Purkayastha, S., Engländer, L., Imhof, T., Vuli, I., Ruder, S., Gurevych, I., & Pfeiffer, J. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.13 · Cited by 39

Efficient transfer learning with pretrained language models

Zhao, M. (2022) · Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München) · doi:10.5282/edoc.30059

An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models

Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1213 · Cited by 108