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

Instance-Aware and Semantic-Guided Prompt for Few-Shot Learning in Large Language Models

Weng, J., Li, D., Deng, Y., Zhang, J., Hu, Y., & Huang, H. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8148-9_5 · Cited by 2

Extrapolating Multilingual Understanding Models as Multilingual Generators

Wu, B., Yuan, F., Zhao, H., Li, L., & Xu, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1031 · Cited by 2

Empowering Legal Citation Recommendation via Efficient Instruction-Tuning of Pre-trained Language Models

Wang, J., Bansal, K., Arapakis, I., Ge, X., & Jose, J. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56027-9_19 · Cited by 2

Wiki-based Prompts for Enhancing Relation Extraction using Language Models

Layegh, A., Payberah, A. H., Soylu, A., Roman, D., & Matskin, M. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3635949 · Cited by 3

LACNER: Enhancing Few-Shot Named Entity Recognition with Label Words and Contrastive Learning

Xiao, Y., Yang, Q., Zou, J., & Zhou, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650197 · Cited by 2

Enhancing Pre-trained ViTs for Downstream Task Adaptation: A Locality-Aware Prompt Learning Method

Wang, S., Yu, Y., He, Y., & Gong, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680983 · Cited by 2

Leveraging Parameter-Efficient Fine-Tuning for Multilingual Abstractive Summarization

Shen, J., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9437-9_23 · Cited by 2

Robust Visual Reinforcement Learning by Prompt Tuning

Tran, T. A., Than, K. Q., & Vargas, D. V. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0969-7_23 · Cited by 2

Bridging Sensor Gaps via Attention-Gated Tuning for Hyperspectral Image Classification

Xue, X., Zhang, H., Jing, H., Tao, L., Bai, Z., & Li, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3542977 · Cited by 2

WhisPrompt: Audio classification of Chinese opera genres by transferring time-series features

Shi, B., Wang, H., Qiu, J., & Li, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127301 · Cited by 3

Knowledge-Enhanced Large Language Model-Based Assistance Training System for Subway Maintenance Personnel

Yang, D. C., Wang, H., Shao, S., & Liu, S. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1145/3716895.3716900 · Cited by 2

Leveraging LLMs for Continuous Data Streams: Methods and Applications

Kumar, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6303-3_33 · Cited by 2

Prompt Learning for Source Code Summarization

Xu, T., Fang, C., Qian, H., Feng, X., & Sun, W. (2025) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs65678.2025.00028 · Cited by 2

MSPLoRA: A Multi-Scale Pyramid Low-Rank Adaptation for Efficient Model Fine-Tuning

Zhao, J. (2025) · International Conference on Intelligent Computing (ICIC) · doi:10.65286/icic.v21i2.84029 · Cited by 4

Graph structure prompt learning: A novel methodology to improve performance of graph neural networks

Huang, Z., Li, K., Wang, S., Jia, Z., Zhu, W., & Mehrotra, S. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06952-x · Cited by 2

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

Enhancing Cross-Lingual Few-Shot Named Entity Recognition by Prompt-Guiding

Wang, Y., Huang, Y., Gong, T., & Li, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44207-0_14 · Cited by 3

Global Prompt Cell: A Portable Control Module for Effective Prompt Tuning

Liu, C., Wang, H., Xi, N., Zhao, S., & Qin, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_51 · Cited by 2

Advanced PromptCBLUE Performance: A Novel Approach Leveraging Large Language Models

Ling, H., Ge, C., Wang, J., Quan, F., & Zeng, J. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7224-1_28 · Cited by 2

Overview of CHIP2023 Shared Task 4: CHIP-YIER Medical Large Language Model Evaluation

Hu, H., Yan, J., Zhang, X., Jiao, Z., & Tang, B. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_11 · Cited by 3

Innovative Design of Large Language Model in the Medical Field Based on chip-PromptCBLUE

Ling, H., Yin, B., Ge, C., Shi, P., Wang, J., Xian, F., & Quan, F. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_2 · Cited by 2

CMed-Baichuan: Task Explanation-Enhanced Prompt Method on PromptCBLUE Benchmark

Ren, X., Song, Y., Yan, C., Xiong, Y., Kong, F., & Fu, X. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_3 · Cited by 2

Attentive Perturbation: Extending Prefix Tuning to Large Language Models Inner Representations

Falissard, L., Affeldt, S., & Nadif, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53969-5_36 · Cited by 2

Beyond Simple Text Style Transfer: Unveiling Compound Text Style Transfer with Prompt-Based Pre-Trained Language Models

Ju, S., & Wang, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447801 · Cited by 1

Research on the Construction of Multi-Scale Fine-Tuning Samples for Electric Power Based on the Chain of Thought

Qiuzhen, Liu, Y., Huang, X., Shang, J., & Jin, M. (2024) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd64080.2024.10702203 · Cited by 1

LogSI: A Benchmark for System-Incremental Log Analysis

Zhou, M., Yang, W., Ma, L., Jiang, S., Xu, B., & Xiao, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890641 · Cited by 1

Injecting Visual Features into Whisper for Parameter-Efficient Noise-Robust Audio-Visual Speech Recognition

Yang, Z., Yeo, Y. H., Jiang, R., Fu, X., Chen, W., Xi, W., & Zhao, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888331 · Cited by 1

Constructing a Shopping Mall Customer Service Center Robot Based on the LLAMA-7B Language Model

Tsai, H., Jhang, J.-W., & Wang, J.-F. (2024) · International Conference on Orange Technology (ICOT) · doi:10.1109/icot64290.2024.10936962 · Cited by 1

Parameter-efficient adaptation with multi-channel adversarial training for far-field speech recognition

Niu, T., Chen, Y., Qu, D., Hu, H., & Liu, C. (2025) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-025-00406-5 · Cited by 1

Prefix-tuning-based Consistency Learning Framework for Machine Reading Comprehension with unanswerable questions

Zhang, Y., & Shen, B. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110567 · Cited by 1

Enhancing MusicGen with Prompt Tuning

Shin, H., Im, J., & Sung, Y. (2025) · Applied Sciences · doi:10.3390/app15158504 · Cited by 1

Memory-Aware Privacy Protection for Large Language Models in Education

Liao, Y., Keung, J., & Chan, W. K. (2025) · International Symposium on Educational Technology · doi:10.1109/iset65607.2025.00043 · Cited by 1

Soft prompt-tuning for plant pest and disease classification from colloquial descriptions

Liu, X., Li, X., & Zhu, Y. Q. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1668642 · Cited by 1

ILD-Slider: A Parameter-Efficient Model for Identifying Progressive Fibrosing Interstitial Lung Disease from Chest CT Slices

Zhang, J., Wada, S., Sugimoto, K., Niitsu, T., Fukushima, K., Kida, H., Wang, B., Konishi, S., Okada, K., Nakashima, Y., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11100353 · Cited by 1

Multimodal Aspect-Based Sentiment Analysis With Plugin-Enhanced Large Language Models

Tian, Y., Song, Y., & Zhang, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3622470 · Cited by 1

Causal-ESC : Capture the Dynamics in Cause-and-Effect Detection for Emotional Support Conversation

Zhao, Z., Xu, Y., Zhao, S., & Sun, X. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3648885 · Cited by 1

LoopRAG: A Closed-Loop Multi-Agent RAG Framework for Interactive Semantic Question Answering in Smart Buildings

Junqi, B. A. I., Ning, D., You, Y., & Chen, J. (2026) · Buildings · doi:10.3390/buildings16010196 · Cited by 1

The Teacher–Student Interactive Cycle: Joint Optimization With Inner-Loop Self-Distillation in Prompted Foundation Models for Efficient Semantic Segmentation

Li, M., Zhao, Q., Lyu, S., Jiang, J., Zou, L., Yao, D., Cheng, G., & Yang, C. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3665905 · Cited by 1

Clara: A Cross-Modal Learning Framework for Enhanced Vulnerability Detection

Peng, X., Wang, S., Lin, B., Qin, Y., Chen, L., & Mao, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3691741 · Cited by 1

GME-Init: Gamma-Moment Equalization for LoRA Initialization in Parameter-Efficient Fine-Tuning

Lin, Y., Li, C., Shen, Z., Liu, J., & Zeng, M. (2026) · Entropy · doi:10.3390/e28080873 · Cited by 1

Simple Domain Adaptation for Sparse Retrievers

Vast, M., Zong, Y., Piwowarski, B., & Soulier, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56063-7_32 · Cited by 1

Prompt-Enhanced Generation for Multimodal Open Question Answering

Cui, C., & Li, Z. (2024) · Electronics · doi:10.3390/electronics13081434 · Cited by 2

TriMPL: Masked Multi-Prompt Learning with Knowledge Mixing for Vision-Language Few-shot Learning

Liu, X., Shang, Y., & Chen, Y. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3658106 · Cited by 1

Medical Cross-Modal Prompt Hashing with Robust Noisy Correspondence Learning

Liu, Y., Wu, Z., Chen, B., Zhang, Z., & Lu, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72384-1_24 · Cited by 1

Plug-and-play adapter for fusing convolutional neural network with vision transformer

Chen, B., Fan, X., & Wu, S. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.5.053050 · Cited by 1

Activate Integrated Controllable Generation with Soft Prompt

Ma, J., Zhan, R., Wong, D. F., & Chao, L. S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9440-9_19 · Cited by 1

PieBridge: Fast and Parameter-Efficient On-Device Training via Proxy Networks

Yin, W., Xu, D., Huang, G., Zhang, Y., Wei, S., Xu, M., & Liu, X. (2024) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3666025.3699327 · Cited by 2

Uniform Graph Pre-training and Prompting for Transferable Recommendation

Yu, Q., Zou, L., Luo, X., Zhao, X., & Li, C. (2025) · ACM Transactions on Information Systems · doi:10.1145/3724392 · Cited by 2

Assessing Diagnostic Consistency in Clinical Cases: A Fine-Tuned LLM Voting and GPT Error Correction Framework

Huang, W., Dai, F., Wu, C., Hu, J., Lyu, Y., Liu, J., & Xue, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4298-4_17 · Cited by 1

A Commit Classification Framework Incorporated With Prompt Tuning and External Knowledge

Tong, J., & Rui, X. (2025) · IET Software · doi:10.1049/sfw2/5566134 · Cited by 2

Knowledge Contrast-Enhanced Continuous Prompt Tuning for few-shot learning

Li, F., Huang, Y., Wang, Y., Chen, Z., Xu, Y., & Li, X. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11251-0 · Cited by 1

Adpl: attentive dual-modality prompt learning for vision-language understanding

Deng, Z., Wang, R., Xue, L., & Yang, J. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07392-7 · Cited by 1

Enhancing Antibiotic Question Answering with Large Language Models

Quan, Z., Yuan, F., Fu, J., Wu, W., & Zhu, X. (2024) · International Conference on Computer, Internet of Things and Smart City (CIoTSC) · doi:10.1145/3731867.3731888 · Cited by 1

Few-shot font generation via stroke prompt and hierarchical representation learning

Zeng, J., Zhang, Y., Yuan, Y., Tu, L., & Wang, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128656 · Cited by 1

Enhancing Prompt Tuning for Smaller Pretrained Models via Knowledge Distillation

Yuan, M., & Lang, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-7030-7_12 · 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

Multi-query frequency prompting for physiological signal domain adaptation

Kang, J., Byun, H., Kim, T., Jung, J., & Song, K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114082 · Cited by 1

RDLoRA:Rank-Decomposed Low-Rank Adaptation for Multi-task Medical Applications

He, Q., & Le, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_29 · Cited by 1

Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning

Wang, J., Luo, T., Zha, Y., Feng, Y., Luo, R., Chen, B., Dai, T., Chen, L., Wang, Y., & Xia, 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/cvpr52734.2025.02342 · Cited by 1

Vision to Specification: Automating the Transition from Conceptual Features to Functional Requirements

Lian, X., Wu, J., Gao, X., Wang, S., & Zhang, L. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3754450 · Cited by 1

Digital footprints and personality prediction: integrating methodological innovations and ethical considerations in social media analysis

Maharani, W., & Gani, P. H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11607-6 · Cited by 1

Research on the optimization of seamless steel pipe cooling process based on large language models

Wu, Q., Chen, X., Luo, M., Li, A., Yuan, Q., Yu, D., & Dai, Q. (2025) · AIP Advances · doi:10.1063/5.0289529 · Cited by 1

FEAM: a dynamic prompting framework for sentiment analysis with hierarchical convolutional attention

Lin, Z., Wu, D., Yang, X., Li, L., & Qin, Z. (2025) · Frontiers in Physics · doi:10.3389/fphy.2025.1674949 · Cited by 1

Hybrid and Unitary PEFT for Resource-Efficient Large Language Models

Qi, H., Dai, Z., & Huang, C. (2025) · American Journal of Computer Science and Technology · doi:10.11648/j.ajcst.20250804.17 · Cited by 1

From Detection to Mitigation: Addressing Gender Bias in Chinese Texts via Efficient Tuning and Voting-Based Rebalancing

Wu, C., Cai, Y., Cheng, Y., & Xue, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_39 · Cited by 1

State-of-Health, State-of-Charge, and State-of-Energy Co-Estimation of Li-Ion Batteries With Pretrained Language Model Empowered by Multistate Explicit-Implicit Prompt Learning

Bian, C., Duan, Z., Yang, S., & Feng, J. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3639605 · Cited by 1

MMtuning: An Advanced Multi-adapter Framework for Efficient Multimodal Large Language Models Fine-Tuning

Qiao, L., Wang, H., Sugiura, K., Liu, K., & Hu, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3055-7_5 · Cited by 1

Association rule mining for aircraft assembly process information based on fine-tuned LLM

Shen, J., Zhao, W. W., Liu, X., Ning, J., & Zhang, Y. (2026) · Autonomous Intelligent Systems · doi:10.1007/s43684-025-00111-2 · Cited by 1

Utilizing large model for content-based identification of disruptive and developmental research

Huang, S., Lu, W., Huang, Y., Liu, Y., Cheng, Q., & Wang, Q. (2026) · Scientometrics · doi:10.1007/s11192-025-05513-w · Cited by 1

AGPL-KEM : Attribute-guided prompt learning with knowledge experts mixture for few-shot remote sensing image classification

Wu, C., Zhu, C., Xu, Q., Liu, X., Zhang, Y., Wang, L., & Wu, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115375 · Cited by 1

ChronoLLM: customizing language models for physics-based simulation code generation

Wang, J., Negrut, A., Zhang, H., Slaton, K., Wang, S., Serban, R., Wu, J., & Negruţ, D. (2026) · Multibody System Dynamics · doi:10.1007/s11044-026-10152-x · Cited by 3

A Comprehensive Approach for Fine-tuning and Evaluation of Large Language Models on Dialogue Summarization

Krithika, S., & Mohan, G. B. (2026) · SN Computer Science · doi:10.1007/s42979-026-04852-6 · Cited by 1

ExoPrompt: Transformer-based greenhouse climate forecasting with structured conditioning and physics-based simulation

Soykan, G., Babur, Ö., Liu, Q., & Teki̇nerdoğan, B. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111673 · Cited by 1

ChatHTN: a consultation model for hypertension

Wang, D., Yuan, Z., Deng, H., & Peng, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-47937-1 · Cited by 1

Improving Legal Judgment Prediction via Quantitative Reasoning

Han, Z., Feng, Y. C., Yang, Y., Li, C., Fei, Z., Ding, X., Ge, J., & Ng, V. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3807945 · Cited by 1

Supervised contrastive learning in few-shot soft prompt tuning

Edalat, A., & Yaghoobzadeh, Y. (2026) · Natural language processing. · doi:10.1017/nlp.2026.10013 · Cited by 1

Reconstructing Training Data from Adapter-based Federated Large Language Models

Chen, S., Luo, Y., Deng, G., Liu, Y., Xu, M., Fu, S., & Jia, X. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792182 · Cited by 1

MLLM-Driven Autonomous Driving: Closed-Loop Decision-Making with Language-State Alignment and Human Instruction Integration

Wu, Y., & Shen, C.-C. (2026) · ACM Journal on Autonomous Transportation Systems · doi:10.1145/3811541 · Cited by 1

TeachPlanAlign: Dual-Profile Personalized and Curriculum-Grounded Lesson Plan Generation Schema via Retrieval-Augmented Fine-Tuning

Fu, S., Liu, F., Wu, H., Zhou, J., & Pan, Z. (2026) · Mathematics · doi:10.3390/math14091492 · Cited by 1

Efficient Event Camera Data Pretraining with Adaptive Prompt Fusion

Liang, Q., Li, Q., Liu, S., Cao, X., Lu, J., Yang, F., Zhang, W., Huang, K., & Tian, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00810 · Cited by 1

A comprehensive review of use cases, misuses, and potential mitigation techniques in generative artificial intelligence

Patel, K., Ajith, R., Thapa, S., Adhikari, S., Tanveer, M., & Naseem, U. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109073 · Cited by 1

Key Technologies of AI Large Language Models in the Field of Traditional Chinese Medicine

Fei, H., Yanhui, L., Junxia, L., Chen, Z., Weirong, X., & Zhang, L. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426590263 · Cited by 1

A survey of parameter-efficient fine-tuning techniques for audio foundation models

Yadla, P. (2026) · International Journal of Speech Technology · doi:10.1007/s10772-025-10245-6 · Cited by 1

DFS-NER: Description Enhanced Few-shot NER via Prompt Learning and Meta-Learning

Huang, H., Feng, Y., Jin, X., Guan, S., & Guo, J. (2022) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat55865.2022.00131 · Cited by 1

Towards Open Environment Intent Prediction

Zhou, Y., Hong, J., & Qiu, X. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.140 · Cited by 1

AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning

Hong, S., & Jang, T. Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.709 · Cited by 1

Contextualized Hybrid Prompt-Tuning for Generation-Based Event Extraction

Zhong, Y., Xu, T., & Luo, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40292-0_31 · Cited by 1

Exploring Prompts in Few-Shot Cross-Linguistic Topic Classification Scenarios

Zhang, Z., Liu, S., & Cheng, J. (2023) · Applied Sciences · doi:10.3390/app13179944 · Cited by 1

Improving generalization in large langue model by learning prefix subspaces

Falissard, L., Guigue, V., & Soulier, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.768 · Cited by 4

FSRD: few-shot fuzzy rumor detection system with stance-enhanced prompt learning

Li, H., Yang, W., Wang, H., & Wang, W. (2024) · Soft Computing · doi:10.1007/s00500-023-09439-4 · Cited by 2

Prompt-Based Learning in Natural Language Processing from 2020 to 2022, a Survey

Wang, K., Du, J., Li, B., Qin, Y. X., Tang, J., Zhou, Y., & Li, L. (2023) · IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) · doi:10.1109/ic-nidc59918.2023.10390863 · Cited by 1

CoPrompt: A Contrast-prompt Tuning Method for Multiparty Dialogue Character Relationship Extraction

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Hot-Fixing Wake Word Recognition for End-to-End ASR Via Neural Model Reprogramming

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Promptformer: Prompted Conformer Transducer for ASR

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STEP: Generating Semantic Text Embeddings with Prompt

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A Simple Method to Improve the Performance of Small Pre-trained Language Models on Few-shot Tasks

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WC-SBERT: Zero-Shot Topic Classification Using SBERT and Light Self-Training on Wikipedia Categories

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Research on the Construction of Knowledge Platform for Pumped Storage Power Station

Lyu, Y. (2024) · International Conference on Computer Engineering and Application (ICCEA) · doi:10.1109/iccea62105.2024.10603963 · Cited by 1

Aspect-level Sentiment Analysis based on Prompt Templates and External Knowledge

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AugSBertChat: User Feedback-Enhanced QA with Sentence-RoBERTa

Zhang, B., Zhao, T., Ma, C., Chen, Y., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5669-8_17 · Cited by 1

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and TasksPreprint

Liu, X., Ji, K., Fu, Y., Tam, W. L., Du, Z., Yang, Z., & Tang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07602 · Cited by 743

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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