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

Overview of the PromptCBLUE Shared Task in CHIP2023

Zhu, W., Wang, X., Chen, M., & Tang, B. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_1 · Cited by 7

Knowledge Graphs Enhanced Large Language Model Prompt for Electric Power Question Answering

Wang, C., Hua, M., Song, J., & Tang, X. (2023) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3650400.3650405 · Cited by 7

SelfCP: Compressing over-limit prompt via the frozen large language model itself

Gao, J., Cao, Z., & Li, W. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103873 · Cited by 7

A Sentimental Prompt Framework with Visual Text Encoder for Multimodal Sentiment Analysis

Huang, S., Xu, B., Li, C., Ye, J., & Lin, X. A. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3658115 · Cited by 7

Research on the Adaptability of Generative Algorithm in Generative Landscape Design

Chen, R., Luo, X., He, Y., & Zhao, J. (2024) · Landscape Architecture · doi:10.3724/j.fjyl.202404120207 · Cited by 9

Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems

Petruzzelli, A. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688023 · Cited by 7

PathoTune: Adapting Visual Foundation Model to Pathological Specialists

Lu, J., Yan, F., Zhang, X., Gao, Y., & Zhang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_37 · Cited by 10

Investigating Large Language Models for Prompt-Based Open-Ended Question Generation in the Technical Domain

Maity, S., Deroy, A., & Sarkar, S. (2024) · SN Computer Science · doi:10.1007/s42979-024-03464-2 · Cited by 7

Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential Recommendation

Zhang, J., Li, C., & Zhao, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3719343 · Cited by 7

Soft-Prompt Tuning to Predict Lung Cancer Using Primary Care Free-Text Dutch Medical Notes

Elfrink, A., Vagliano, I., Abu–Hanna, A., & Calixto, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34344-5_23 · Cited by 7

Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-Physical Systems

Xu, Q., Yue, T., Ali, S., & Arratibel, M. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3388572 · Cited by 9

ProTeCt: Prompt Tuning for Taxonomic Open Set Classification

Wu, T.-Y., Ho, C.-H., & Vasconcelos, N. M. (2024) · 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/cvpr52733.2024.01564 · Cited by 10

FreestyleRet: Retrieving Images from Style-Diversified Queries

Li, H., Jia, Y., Jin, P., Cheng, Z., Li, K., Sui, J., Liu, C., & Yuan, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_15 · Cited by 9

Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

Jiang, J., Qu, S., Cheng, M., Liu, Q., Liu, Z., Zhang, H., Zhang, R., Zhang, K., Li, R., Li, J., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-5555-4_25 · Cited by 7

CKEMI: Concept knowledge enhanced metaphor identification framework

Wang, D., Li, Y., Wang, S., Chen, X., Li, D., Li, D., & Li, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103946 · Cited by 6

XuanHuGPT: parameter-efficient fine-tuning of large language model in the field of traditional Chinese medicine

Tong, X., Ding, X., Jia, H., Yuan, Y., Liu, L., Wang, Y., Xiong, Z., Yang, X., Im, S., & Wang, H. (2025) · Chinese Medicine · doi:10.1186/s13020-025-01200-3 · Cited by 6

Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning

Zhao, H., Fu, J., & He, Z. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.280 · Cited by 11

SRAG: Speech Retrieval Augmented Generation for Spoken Language Understanding

Yang, H., Zhang, M., Wei, D., & Guo, J. (2024) · IEEE International Conference on Control, Electronics and Computer Technology (ICCECT) · doi:10.1109/iccect60629.2024.10546001 · Cited by 7

Augmenting Low-Resource Cross-Lingual Summarization with Progression-Grounded Training and Prompting

Ma, J., Huang, Y., Wang, L., Huang, X., Peng, H., Yu, Z., & Yu, P. S. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3675167 · Cited by 6

Hierarchical Mutual Prompt for Chinese Few-Shot Event Detection

Hou, S., Qian, Y., Chen, J., Zhao, J., Lv, H., Lü, Y., & Leng, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5672-8_33 · Cited by 6

EADRE: Event-type Aware Dynamic Representation of Entities in Document-level Event Extraction

Zhang, G., Hu, Z., Li, R., & Tan, H. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3695767 · Cited by 6

Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection

Peng, X., Bai, Y., Gao, C., Yang, L., Xia, F., Mu, B., Wang, X., & Liu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_21 · Cited by 6

Any2Point: Empowering Any-Modality Large Models for Efficient 3D Understanding

Tang, Y., Zhang, R., Liu, J., Guo, Z., Zhao, B., Wang, Z., Gao, P., Li, H., Wang, D., & Li, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72764-1_26 · Cited by 12

Calibrating Prompt from History for Continual Vision-Language Retrieval and Grounding

Jin, T., Yan, W. B., Wang, Y., Cai, S., Shuai, Q., & Zhao, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681387 · Cited by 6

End-Cloud Collaboration Framework for Advanced AI Customer Service in E-commerce

Teng, L., Liu, Y., Liu, J., & Song, L. (2024) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot62078.2024.10811144 · Cited by 7

Impact of Noisy Supervision in Foundation Model Learning

Chen, H., Wang, Z., Tao, R., Wei, H., Xie, X., Sugiyama, M., Raj, B., & Wang, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3552309 · Cited by 14

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

No clues good clues: out of context Lexical Relation Classification

Pitarch, L., Bernad, J., Dranca, L., Bobed, C., & Gracia, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.308 · Cited by 6

Knowledgeable Parameter Efficient Tuning Network for Commonsense Question Answering

Zhao, Z., Hu, L., Zhao, H., Shao, Y., & Wang, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.503 · Cited by 6

Dialect Adaptation and Data Augmentation for Low-Resource ASR: Taltech Systems for the Madasr 2023 Challenge

Alumäe, T., Kong, J., & Rõbnikov, D. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389668 · Cited by 7

Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning

Yang, B., Dai, Y., Cheng, X., Li, Y., Raza, A., & Zou, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28466 · Cited by 6

TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding

Wang, S., Li, J., Zhao, Z., Lian, D., Huang, B., Wang, X., Li, Z., & Gao, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00097 · Cited by 9

Advances in edge-cloud collaboration and evolution for large-small models

Wang, Y., Shen, T., Zhang, S., Wu, F., Zhao, Z., Cai, H., Lyu, C., Ma, L., Yang, C., & Wu, F. L. (2024) · Journal of Image and Graphics · doi:10.11834/jig.240011 · Cited by 8

Prompt-Based Self-training Framework for Few-Shot Named Entity Recognition

Huang, G., Zhong, J., Wang, C., Dai, Q., & Li, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10989-8_8 · Cited by 6

Prompt Discriminative Language Models for Domain Adaptation

Lu, K., Potash, P., Lin, X., Sun, Y., Qian, Z., Zheng, Y. G., Naumann, T., Cai, T., & Lu, J. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.30 · Cited by 7

Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning

Zhang, H., Li, D., Li, Y., Shang, C., Shi, C., & Jiang, Y. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.23 · Cited by 6

Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Kim, T., Marsden, M. A., Ahn, P., Kim, S., Lee, S., Sala, A., & Kim, S. H. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00733 · Cited by 6

Dlap: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability DetectionPreprint

Yang, Y., Zhou, X., Mao, R., Xu, J., Yang, L., Zhang, Y., Shen, H., & Zhang, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4831591 · Cited by 6

Georeferencing complex relative locality descriptions with large language models

Fernando, A., Ranathunga, S., Stock, K. M., Prasanna, R., & Jones, C. B. (2026) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2026.2613291 · Cited by 7

MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models

Guo, Y., & Gu, X. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02586-1 · Cited by 5

Advancing Healthcare with Large Language Models: Techniques and Application

Hu, Z., Peng, Z., Bi, Z., Shen, Q., Liu, Z., Lou, J., & Luo, X. (2025) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2025.125540 · Cited by 5

Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service

Ha, H., Lee, J., Han, W., & Chun, B.-G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.159 · Cited by 5

CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models

Ormazabal, A., Artetxe, M., & Agirre, E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.180 · Cited by 12

Enhancing Multimodal Tourism Review Sentiment Analysis Through Advanced Feature Association Techniques

Chen, P., & Fu, L. (2024) · International Journal of Information Systems in the Service Sector · doi:10.4018/ijisss.349564 · Cited by 6

Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-Time Adaptation

Tang, Y., Chen, S., Kan, Z., Zhang, Y., Guo, Q., & He, Z. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3443633 · Cited by 7

Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach

Zhang, T., Bai, J., Lu, Z., Lian, D., Wang, G., Wang, X., & Xia, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72995-9_20 · Cited by 7

KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

Jeon, D. H., Sun, W., Song, H. H., Liu, D., Alvaro, V., Xie, Y., & Niu, S. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825929 · Cited by 5

UniPET-SPK: A Unified Framework for Parameter-Efficient Tuning of Pre-Trained Speech Models for Robust Speaker Verification

Sang, M., & Hansen, J. H. L. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3539005 · Cited by 5

Smart contract generation model based on code annotation and AST-LSTM tuning

Yong, C., Defeng, H., Xu, C., Nannan, C., Shen, F., & Liu, J. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07186-x · Cited by 5

Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation

Zhang, B., Ma, H., Li, D., Ding, J., Wang, J., Xu, B., & Lin, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.17 · Cited by 5

Chameleon: Adaptive Caching and Scheduling for Many-Adapter LLM Inference Environments

Iliakopoulou, N., Stojkovic, J., Alverti, C., Xu, T., Franke, H., & Torrellas, J. (2025) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3725843.3756083 · Cited by 15

Leveraging Large Language Models for Hate Speech Detection: Multi-Agent, Information-Theoretic Prompt Learning for Enhancing Contextual Understanding

Lee, K., & Ram, S. (2025) · Journal of Management Information Systems · doi:10.1080/07421222.2025.2561383 · Cited by 5

Developing Prefix-Tuning Models for Hierarchical Text Classification

Chen, L., Chou, H. W., & Zhu, X. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.39 · Cited by 5

Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering

Yuan, B., You, S., & Bao, B. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612222 · Cited by 5

Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding

Wang, Y., Peng, X., Shen, T., Clarke, A., Schlegel, C., Martin, P., & Long, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46671-7_2 · 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

Enhancing educational Q&A systems using a Chaotic Fuzzy Logic-Augmented large language model

Chen, H., Shi, N., Chen, L., & Lee, R. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1404940 · Cited by 5

Carbon Price Forecasting with LLM-Based Refinement and Transfer-Learning

Jiang, H., Ding, Y., Chen, R., & Fan, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72356-8_10 · Cited by 5

MPE 3 : Learning meta-prompt with entity-enhanced semantics for few-shot named entity recognition

Xia, Y., Zhao, T., Wang, L., Liu, Q., Wu, S., & Zhang, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129031 · Cited by 5

Improving multi-hop question answering with prompting explicit and implicit knowledge aligned human reading comprehension

Huang, G., Long, Y., & Luo, C. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02712-y · Cited by 6

Causal representation for few-shot text classification

Yang, M., Zhang, X., Wang, J., & Zhou, X. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04667-5 · Cited by 5

Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation

Aksu, T., Kan, M., & Chen, N. F. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.252 · Cited by 5

Tuning in to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language

Sun, J., Zhang, X., & Moens, M. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230524 · Cited by 6

Revision Transformers: Instructing Language Models to Change Their Values

Friedrich, F., Stammer, W., Schramowski, P., & Kersting, K. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230341 · Cited by 6

Prompt Learning Under the Large Language Model

Sun, L., & Shi, Z. (2023) · International Seminar on Computer Science and Engineering Technology (SCSET) · doi:10.1109/scset58950.2023.00070 · Cited by 5

CPL-NoViD: Context-Aware Prompt-Based Learning for Norm Violation Detection in Online Communities

He, Z., May, J., & Lerman, K. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31335 · Cited by 5

Differentiable Data Augmentation for Contrastive Sentence Representation Learning

Wang, T., & Lü, W. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.520 · Cited by 5

TKG: Telecom Knowledge Governance Framework for LLM ApplicationPreprint

Cai, H., & Wu, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3252192/v1 · Cited by 6

Personalised soft prompt tuning in pre-trained language models: Bridging multitask transfer learning and crowdsourcing learning

Tian, Z., Zhang, H., & Wang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112646 · Cited by 5

Variational Dehallucination via Retrieval-Augmented Prompt Learning

Lin, H.-Y., Chang, Y.-C., & Chien, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3646477 · Cited by 4

Reasoning-Guided Prompt Learning with Historical Knowledge Injection for Ancient Chinese Relation Extraction

Zhou, Z., & Zhou, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_14 · Cited by 4

Test-Time Prompt Tuning for Zero-Shot Depth Completion

Jeong, C., Bae, I., Park, J.-H., & Jeon, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00881 · Cited by 4

Significant ASR Error Detection for Conversational Voice Assistants

Harvill, J. B., Khaziev, R., Li, S., Cogill, R., Wang, L., Chennupati, G., & Thadakamalla, H. P. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448230 · Cited by 4

Deferred Continuous Batching in Resource-Efficient Large Language Model Serving

He, Y., Lu, Y., & Alonso, G. (2024) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3642970.3655835 · Cited by 4

LLM-Enhanced Chinese NL2SQL Translation Task Under Resource-Limited Condition

Xiao, X., & Xu, H. (2024) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3674399.3674433 · Cited by 4

Neural Collapse Anchored Prompt Tuning for Generalizable Vision-Language Models

Zhu, D., Li, Z., Zhang, M., Yuan, J., Liu, J., Kuang, K., & Wu, C. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671690 · Cited by 4

Unified Multi-Scenario Summarization Evaluation and Explanation

Shang, S., Yao, Z., Fu, H., Tao, C., Chen, X., Wang, F., Wang, Y., Ren, Z., & Gao, S. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3509715 · Cited by 4

PT4Rec: a universal prompt-tuning framework for graph contrastive learning-based recommendations

Wei, X., & Zhou, Q. (2025) · Machine Learning · doi:10.1007/s10994-024-06658-0 · Cited by 4

Identifying potentially disruptive research via a comparative power-based large model

Huang, S., Lu, W. P., Xu, Z., Cheng, Q., Yang, J., & Huang, Y. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104207 · Cited by 6

WamGLM: A multimodal large-scale language model for wafer map defect information in-depth query through multi-turn dialogue based on prototypical supervised contrastive learning

Gu, S., Lei, Z., Wen, G., Xu, Q., Li, Z. S., Chen, X., & Yang, C. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113962 · Cited by 5

A smaller model can be better: Domain adaptation for LLM-generated text detection via soft prompt-tuning

Wang, S., Zhu, Y., & Li, P. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104566 · Cited by 4

ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding

Wang, Y., Jiang, X., Cheng, D., Li, D., & Zhao, C. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3671609 · Cited by 5

Beyond the screen: Exploring the impact of GenAI-supported online learning on interaction, motivation, and self-regulation in higher education

Arslan, M. A., Arpacık, Ö., Küçük, S., & Durak, H. Y. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2643173 · Cited by 4

AI-driven design: a comprehensive review of its methodologies, applications, and challenges

Yan, J., Hu, W., Zhao, Y., Zhang, T., Zhao, F., Fang, J., Shi, W. Y., & Tan, J. (2026) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-026-04331-9 · Cited by 4

Reassessing automated essay scoring with large language models: Evidence from API and GUI interfaces

Kim, J., & Holden, D. (2026) · Assessing Writing · doi:10.1016/j.asw.2026.101080 · Cited by 4

Multi-Stage Prompt Tuning for Political Perspective Detection in Low-Resource Settings

Kim, K.-M., Lee, M., Won, H.-S., Kim, M., Kim, Y., & Lee, S. (2023) · Applied Sciences · doi:10.3390/app13106252 · Cited by 6

FQN Inference in Partial Code by Prompt-tuned Language Model of Code

Huang, Q. Y., Yuan, Z., Xing, Z., Peng, X., Xu, X., & Lu, Q. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3617174 · Cited by 4

Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction

Kim, J., Kim, G., Son, J., & Lim, H. (2023) · Applied Sciences · doi:10.3390/app132212414 · Cited by 4

Does the English Matter? Elicit Cross-lingual Abilities of Large Language Models

Ranaldi, L., & Pucci, G. (2023) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2023.mrl-1.14 · Cited by 6

Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression

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