P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks

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

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

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

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473

Continuous Prompt Tuning for Russian: How to Learn Prompts Efficiently with RuGPT3?

Konodyuk, N., & Tikhonova, M. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-15168-2_3 · Cited by 4

A Sequence-to-Sequence Approach with Mixed Pointers to Topic Segmentation and Segment Labeling

Xia, J., & Wang, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599245 · Cited by 4

Multi-Target Domain Adaptation with Prompt Learning for Medical Image Segmentation

Lin, Y., Nie, D., Liu, Y., Yang, M., Zhang, D., & Wen, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_68 · Cited by 4

Hierarchical Prompt Tuning for Few-Shot Multi-Task Learning

Liu, J., Chen, T., Liang, Z., Jiang, H., Xiao, Y., Feng, W., Qian, Y., Hao, Z., & Han, B. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614913 · Cited by 4

ViM: Vision Middleware for Unified Downstream Transferring

Feng, Y., Gong, B., Jiang, J., Lv, Y., Shen, Y., Zhao, D., & Zhou, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01074 · Cited by 4

Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text Classification

Dai, L., Yin, Y., Chen, E., & Xiong, H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657934 · Cited by 5

To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models

Wang, Z., Ma, W., & Zhang, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5569-1_2 · Cited by 6

Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning

Gupta, U., Galstyan, A., & Steeg, G. V. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.799 · Cited by 4

Multi-task Perception for Autonomous Driving

Liang, X., Liang, X., & Xu, H. (2023) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-99-4287-9_9 · Cited by 4

PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer

Chen, L., Chen, J., Huang, H., & Cheng, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.833 · Cited by 12

Unleashing the Power of Language Models in Clinical Settings: A Trailblazing Evaluation Unveiling Novel Test DesignPreprint

Li, Q., Tang, K. L., Li, S., Zhang, K., Li, Z., Chang, L., Li, W., Shen, B., Ding, J., & Min, X. (2023) · medRxiv · doi:10.1101/2023.07.11.23292512 · Cited by 4

Exploration of ChatGPT application in diabetes education: a multi-dataset, multi-reviewer studyPreprint

Ying, Z., Fan, Y., Lu, J., Wang, P., Zou, L., Tang, Q., Chen, Y., Li, X., & Chen, Y. (2023) · medRxiv · doi:10.1101/2023.09.27.23296144 · Cited by 4

Enriching large language models with semantic lexicons and analogies

Zervakis, G. (2023) · theses.fr (ABES) · doi:10.70675/4f4cd0e5z5987z422az813bzc6e431d1a918 · Cited by 4

Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward

Fan, Z., Wang, Y., Polisetty, S., & Fung, Y. R. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.1037 · Cited by 4

Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt

Zhao, Z., Shi, Z., Liu, Y., & Ding, W. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3612914 · Cited by 3

FedGPL: Gradient Priority-based Federated Learning Enhancement for In-Vehicle Language Models

Yang, H., Xue, D., Wang, T., Kwak, J., & Kim, H. (2025) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3717835 · Cited by 3

CE-FFT: Communication-Efficient Federated Fine-Tuning for Large Language Models via Quantization and In-Context Learning

Zhang, P., Liu, Y., Zhou, Y., Hu, M., Wei, X., & Chen, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889971 · Cited by 3

Enhanced Multimodal Depression Detection With Emotion Prompts

Teng, S., Liu, J., Sun, H., Chai, S., Tateyama, T., Lin, L., & Chen, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889035 · Cited by 5

Leveraging Large Language Models for Clinical Trial Eligibility Criteria Classification

Ray, S., Sarker, A. N., Chatterjee, N., Bhowmik, K., & Dey, S. (2025) · Digital · doi:10.3390/digital5020012 · Cited by 3

A method of legal judgment prediction via prompt learning and charge keywords fusion

Li, J., & Ouyang, J. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09469-3 · Cited by 3

Enhancing Domain Generalization in Medical Image Segmentation With Global and Local Prompts

Zhao, C., & Li, X. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3590956 · Cited by 3

Visual Consensus Prompting for Co-Salient Object Detection

Wang, J., Yu, N., Zhang, Z., & Han, Y. (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/cvpr52734.2025.00896 · Cited by 3

A Federated Adaptive Large Language Model Fine-Tuning Framework for Software Development

Chen, J., Cai, Z., Chen, W., Wang, W., Zheng, Z., & Yu, P. S. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3623626 · Cited by 3

Intelligent generation and reasoning method for aviation assembly knowledge graph based on joint knowledge embedding learning

Liu, P., Zhang, Y., Yang, Y., Zhao, X., & Tao, B. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114140 · Cited by 3

Zero-shot forecasting of volatile wind power against data missing with large language model through attentive residual prompt tuning

Duan, Z., Bian, C., & Yang, S. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.124808 · Cited by 4

Prompt-Augmented LLMs with RAG for Addressing Cold-Start and Sparsity in Online Recommender Systems

Shehmir, S., & Kashef, R. F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14107-1_21 · Cited by 3

Learning from Failure: Improving Meeting Summarization without Good Samples

Wang, K., Zhao, X., & Peng, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29883 · Cited by 3

A Bidirectional Question-Answering System using Large Language Models and Knowledge Graphs

Han, L., Wang, X., Li, Z., Zhang, H., & Chen, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-2991-3_1 · Cited by 3

Rethinking Image-to-Video Adaptation: An Object-Centric Perspective

Qian, R., Ding, S., & Lin, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_19 · Cited by 5

PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning

Liu, H., Gan, J., Fan, X., Zhang, Y., Luo, C., Zhang, J., Jiang, G., Qian, Y., Zhao, C., Ma, Z., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-5779-4_10 · Cited by 5

A Unified Prompt-aware Framework for Personalized Search and Explanation Generation

Zhang, H., Zhu, Q., & Dou, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3716131 · Cited by 3

Automatic analysis of alarm embedded with large language model in police robot

Liu, Z., Sun, H., & Yuan, D. (2025) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2025.100220 · Cited by 4

A Human–Robot Team Knowledge-Enhanced Large Language Model for Fault Analysis in Lunar Surface Exploration

Wang, H., Xue, S., Zhang, H., Wang, C., & Fu, Y. H. (2025) · Aerospace · doi:10.3390/aerospace12040325 · Cited by 4

EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens

Yang, C., Lin, X., Wang, W., Li, Y., Sun, T., Han, X., & Chua, T. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736919 · Cited by 8

Unsupervised Dual Modality Prompt Learning for Facial Expression Recognition

Shahid, M. (2023) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs57501.2023.10151313 · Cited by 3

MDC at BioLaySumm Task 1: Evaluating GPT Models for Biomedical Lay Summarization

Turbitt, O., Bevan, R., & Aboshokor, M. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.65 · Cited by 5

Is Prompt the Future?

Zhen, Z., Wang, L., Gu, D., Wu, H. K., Janfada, B., & Bidgoli, B. M. (2023) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.328681 · Cited by 3

Generative Event Extraction via Internal Knowledge-Enhanced Prompt Learning

Song, H., Zhu, Q., Yu, Z., Liang, J., & He, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44192-9_8 · Cited by 3

A Military Domain Knowledge-Based Question Answering Method Based on Large Language Model Enhancement

Liu, Y., Zhang, J., Hu, F., Li, T., & Wang, Z. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7224-1_27 · Cited by 3

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

Electrical Equipment Fault Diagnosis: A Technique Combining Fuzzy Logic and Large Language Models

Xu, T., & Tang, X. (2023) · IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA) · doi:10.1109/ispce-asia60405.2023.10365878 · Cited by 3

Exploration and Practice of Applying Large Language Models in Home Education Guidance

Wang, G., Qin, S., & Liu, X. (2023) · International Conference on Information Science and Education (ICISE-IE) · doi:10.1109/icise-ie60962.2023.10456439 · Cited by 3

Chinese Diabetes Question Classification Using Large Language Models and Transfer Learning

Ge, C., Ling, H., Quan, F., & Zeng, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_19 · Cited by 3

Cloud-Edge Collaborative Continual Adaptation for ITS Object Detection

Lian, Z., Lv, M., Xu, X., Ding, Z., Zhu, M., Wu, Y., & Yan, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2966-1_2 · Cited by 3

iterPrompt: An iterative prompt-tuning method for nested relation extraction with dynamic assignment strategy

Mai, C., Wang, Y., Gong, Z., Wang, H., Luo, K., Yuan, C., & Huang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124335 · Cited by 4

APT: Adaptive Prefix-Tuning on Pretrained Models for Code Intelligence

Wu, Y., Yu, Y., Yuan, Z., Huang, S., & Cai, B. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651243 · Cited by 4

Three Things We Need to Know About Transferring Stable Diffusion to Visual Dense Prediction Tasks

Zhang, M., Song, G., Shi, X., Liu, Y., & Li, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_8 · Cited by 6

Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction

Kim, Y., Choi, G., & Hwang, S. J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72104-5_50 · Cited by 3

Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning

Liu, H., Gong, S., Ji, Y., Wu, K., Xu, J., & Gu, J. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3680052 · Cited by 4

TARGET: Template-Transferable Backdoor Attack Against Prompt-Based NLP Models via GPT4

Tan, Z., Chen, Q., Huang, Y., & Chen, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9434-8_31 · Cited by 3

Cross-language few-shot intent recognition via prompt-based tuning

Cao, P., Li, Y., & Li, X. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06089-3 · Cited by 3

A few-shot learning method based on knowledge graph in large language models

Wang, F., Shi, D., Aguilar, J., & Cui, X. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00699-3 · Cited by 3

TDA-GLM: text data augmentation for aquaculture disease prevention and control via a small model-guided ChatGLM

Qiao, S., Yu, H., Song, J., Zhang, L., Zhao, H., & Huang, W. (2025) · Aquaculture International · doi:10.1007/s10499-025-01945-6 · Cited by 3

NVCiM-PT: An NVCiM-Assisted Prompt Tuning Framework for Edge LLMs

Qin, R., Ren, P., Yan, Z., Liu, L., Liu, D., Nassereldine, A., Xiong, J., Ni, K., Hu, X. S., & Shi, Y. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10993249 · Cited by 3

Using Bidirectional Prompt Learning in NLP Few Shot Tasks

Li, D., & Ye, S. (2023) · Frontiers in Computing and Intelligent Systems · doi:10.54097/fcis.v3i1.6362 · Cited by 3

PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent

Liu, G., Xue, Z., Zhang, X., Johnson, K., & Wang, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.748 · Cited by 5

ArgumentPrompt: Activating Multi-category of Information for Event Argument Extraction with Automatically Generated Prompts

Dong, S., Yu, W., Tu, H., Wang, X., Zhou, Y., Hai-li, L., Zhou, J., & Chang, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17120-8_25 · Cited by 3

Relational Soft Prompt Pre-trained Language Model for Knowledge Graph CompletionPreprint

Liu, Y., & Li, L. (2024) · Research Square · doi:10.21203/rs.3.rs-5649957/v1 · Cited by 4

External Knowledge Augmented Polyphone Disambiguation Using Large Language Model

Li, C. (2024) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw62465.2024.10626858 · Cited by 2

PETIS: Intent Classification and Slot Filling for Pet Care Services

Zaman, N., Park, S.-J., Won, H.-S., Kim, M., An, H.-S., & Kim, K.-M. (2024) · IEEE Access · doi:10.1109/access.2024.3452771 · Cited by 2

An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt Engineering

Geng, H., Shi, C., Jiang, X., Kong, Z., & Liu, S. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831494 · Cited by 3

SPIRIT: Structural Entropy Guided Prefix Tuning for Hierarchical Text Classification

Zhu, H., Xia, J., Liu, R., & Deng, B. (2025) · Entropy · doi:10.3390/e27020128 · Cited by 2

P-Distill: Efficient and Effective Prompt Tuning Using Knowledge Distillation

Won, H.-S., Choi, J.-Y., Zaman, N., Aliyeva, D., & Kim, K.-M. (2025) · Applied Sciences · doi:10.3390/app15052420 · Cited by 2

An Automatic Model for Lesson Plans Generation Based on Logical Chains and Prompt Tuning

Ye, F., Cui, H., Ke, C., Zhong, W., Zhang, S., & Zhang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-4407-0_21 · Cited by 2

Multi-level prompting: Enhancing model performance through hierarchical instruction integration

Son, G., & Kim, M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113545 · 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

Federated Discrete Prompt Tuning for Language Models Using Synthetic Examples

Tanimura, T., Nakano, W., Kitagawa, Y., & Takase, M. (2025) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc54725.2025.10975936 · Cited by 2

Improving text processing via adversarial low-rank adaptation

Wu, H., Luo, X., Gao, J., & Huang, D. K. (2025) · Machine Learning · doi:10.1007/s10994-025-06817-x · Cited by 2

AgentTester: An LLM-Based Tool for Unit Test Generation with Automatically Generated Prompts

Chen, H., Chen, K., Zhang, F., Wang, T., & Cheng, L. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-0011-6_10 · Cited by 2

Beyond text: Fusing multi-modal legal knowledge for legal judgment prediction

Zhao, Q., Gao, T., & Guo, N. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114358 · Cited by 2

KEFT: Knowledge-Enhanced Fine-Tuning for Large Language Models in Domain-Specific Question Answering

Li, H., Zhang, J., Shen, H., Cheng, K., & Huang, X. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.31 · Cited by 2

Optimization of Maritime Target Element Resolution Strategies for Non-Uniform Sampling Based on Large Language Model Fine-Tuning

Han, Z., Yu, H., & He, Q. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13101865 · Cited by 2

A Comprehensive Review of Key Technologies for Robot Motion Planning in Contact Tasks in Industrial Automation Scenarios

Jin, S., Ke, K., Gao, B.-Y., Fu, L., & Huang, X. (2025) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-025-01356-x · Cited by 2

Efficient Low-Rank Adaptation for Sparse Large Language Model

Hu, Y., Tian, T., Chen, X., Zhao, Z., Tao, T., Zhang, W., Li, Y., Liang, Y., Li, C., Chen, H., et al. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010174 · Cited by 2

Modality Plug-and-Play: Runtime Modality Adaptation in LLM-Driven Autonomous Mobile Systems

Huang, K., Yin, X., Huang, H., & Gao, W. (2025) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3680207.3723491 · Cited by 2

Multi-Affection Prompt Learning for Sentiment, Emotion, and Sarcasm Joint Detection in Conversations

Zhang, Y., Yang, Y., Wang, X., Li, X., Liang, H., & Tiwari, P. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010196 · Cited by 2

Vision model fine-tuning based on two-level prompts fusion

Mao, K., & Fang, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115185 · Cited by 2

An uncertainty-aware framework integrating large language model and fuzzy inference system for commonsense reasoning

Song, J., Tang, X., Hao, K., & Li, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131273 · Cited by 2

Adapting LLMs for biomedical natural language processing: a comprehensive benchmark study on fine-tuning methods

Zhu, J., Li, J., Zhao, S., Deng, Y., Miao, Y., & Xu, J. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-025-08182-x · 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

LADC-Net: Lesion-aware dual-calibrated network for long-tailed medical image classification

Bai, Y., Li, J., Kong, D., Yang, S., Liu, T., & Yin, B. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113975 · Cited by 2

Multi-Classification of Electric Power Metadata based on Prompt-tuning

Liang, X., Zhang, W., Lei, S., Zhang, Y., Xu, M., Peng, L., & Feng, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23504-7_8 · Cited by 2

Improving Adaptive Knowledge Graph Construction via Large Language Models with Multiple Views

Chen, Y., Cui, S., Huang, K., Wang, S., Tang, C., Liu, T., & Fang, B. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7224-1_21 · Cited by 2

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts

Jiang, G., Jiang, C., Xue, S., Zhang, J., Zhou, J. W., Lian, D., & Wei, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.808 · Cited by 2

Parameter Efficient Point Cloud Prompt Tuning for Unified Point Cloud Understanding

Fei, B., Liu, L., Yang, W., Li, Z., Chen, W., & Ma, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3411773 · Cited by 2

Fine-Tuning Point Cloud Transformers with Dynamic Aggregation

Fei, J., & Deng, Z. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610767 · Cited by 2

CPLCS: Contrastive Prompt Learning-based Code Search with Cross-modal Interaction Mechanism

Zhang, Y., Liu, Y., Fan, X., & Lu, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650201 · Cited by 2

An Active Learning Framework for Continuous Rapid Rumor Detection in Evolving Social Media

Shao, Z., Cai, G., Liu, Q., & Shang, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650445 · Cited by 2

Large Language Model-Driven Semi-Automated Construction of Named Entity Datasets in Specific Knowledge Domain

Liang, Y., & Cao, H. (2024) · International Symposium on Computer and Information Processing Technology (ISCIPT) · doi:10.1109/iscipt61983.2024.10673354 · Cited by 2

A novel backdoor scenario target the vulnerability of Prompt-as-a-Service for code intelligence models

Fang, Y., Feng, Z., Fan, G., & Chen, S. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00138 · Cited by 2

Mixture of Hybrid Prompts for Cross-Domain Aspect Sentiment Triplet Extraction

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Enhancing Clinical Information for Zero-Shot Medical Diagnosis by Prompting Large Language Model

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Educating Language Models as Promoters: Multi-Aspect Instruction Alignment With Self-Augmentation

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Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning

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Adapting Knowledge Prompt Tuning for Enhanced Automated Program Repair

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Continuous Prompt Enhanced Biomedical Entity Normalization

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MTPL-G2T: Graph-to-Text Generation Task Based on Mixed Template Prompt Learning

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GPT understands, too

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LIPT: Improving Prompt Tuning with Late Inception Reparameterization

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XPrompt: Exploring the Extreme of Prompt Tuning

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On Transferability of Prompt Tuning for Natural Language Processing

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