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

Generative language models potential for requirement engineering applications: insights into current strengths and limitations

Saleem, S., Asim, M. N., van Elst, L., & Dengel, A. R. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01707-6 · Cited by 13

Enhancing large language model for fake news video detection via cross-modal retrieval

Han, L., Zhang, X., Wang, T., Liu, Y., & Dong, Z. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104471 · Cited by 13

DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embedding

Choi, J., Hong, J. H., & Ro, Y. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00718 · Cited by 20

ShennongMGS: An LLM-based Chinese Medication Guidance System

Dou, Y., Huang, Y., Zhao, X., Zou, H., Shang, J., Lu, Y., Yang, X., Xiao, J., & Peng, S. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3658451 · Cited by 13

On-device query intent prediction with lightweight LLMs to support ubiquitous conversations

Dubiel, M., Barghouti, Y., Kudryavtseva, K., & Leiva, L. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-63380-6 · Cited by 15

LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication

Wu, P., Liu, Q., Dong, Y., Wang, Z., & Wang, F. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3418881 · Cited by 13

SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking

Guo, G., Li, Z., An, W., Wang, Y., He, X., Luo, Y., Ling, Q., Li, M., & Lin, Z. (2024) · Remote Sensing · doi:10.3390/rs16162975 · Cited by 14

Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles

Zhang, H., Zhao, Y., Sun, B., Wu, Y., Fu, Z., & Xiao, X. (2025) · Applied Sciences · doi:10.3390/app15074034 · Cited by 15

A Survey on the Impact of Pre-Trained Language Models in Sentiment Classification Task

Gautam, H., Gaur, A., & Yadav, D. K. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00805-z · Cited by 14

$$\cal{Y}$$-Tuning: an efficient tuning paradigm for large-scale pre-trained models via label representation learning

Liu, Y., An, C., & Qiu, X. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-3131-8 · Cited by 13

Enhancing Stability and Resource Efficiency in LLM Training for Edge-Assisted Mobile Systems

Liu, C., & Zhao, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3570376 · Cited by 12

Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition

Mai, Z., Zhang, P., Tu, C.-H., Chen, H.-Y., Nguyen, Q.-H., Zhang, L., & Chao, W. (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/cvpr52734.2025.01383 · Cited by 15

Large language models for PHM: a review of optimization techniques and applications

Yu, T., Tang, J., Yu, Q., Li, L., Liu, Y., & Poler, R. (2025) · Autonomous Intelligent Systems · doi:10.1007/s43684-025-00100-5 · Cited by 13

ShennongGPT: A Tuning Chinese LLM for Medication Guidance

Dou, Y., Zhao, X., Zou, H., Xiao, J. Y., Peng, X., & Peng, S. (2023) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai59581.2023.00017 · Cited by 12

Infusing internalized knowledge of language models into hybrid prompts for knowledgeable dialogue generation

Bai, J., Yan, Z., Zhang, S., Yang, J., Guo, H., & Li, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111874 · Cited by 13

Chinese named entity recognition for agricultural diseases based on entity-related visual prompts injection

Zhang, C., Zhang, L., Wu, H., Wang, C., Chen, C., Zhu, H., & Liang, F. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109493 · Cited by 12

ChEX: Interactive Localization and Region Description in Chest X-Rays

Müller, P., Kaissis, G. A., & Rueckert, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72664-4_6 · Cited by 19

Cascade Prompt Learning for Vision-Language Model Adaptation

Wu, G., Zhang, X., Li, Z., Chen, Z., Liang, J., Yang, J. J., & Li, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72973-7_18 · Cited by 16

Chain-of-Factors Paper-Reviewer Matching

Zhang, Y., Shen, Y., Kang, S., Chen, X., Jin, B., & Han, J. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714708 · Cited by 12

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning

Lu, X., Brahman, F., West, P., Jung, J., Chandu, K. R., Ravichander, A., Ammanabrolu, P., Jiang, L., Ramnath, S., Dziri, N., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.424 · Cited by 30

Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning

Yao, Z., Yang, W., & Wei, F. (2024) · Entropy · doi:10.3390/e26040325 · Cited by 12

Black-Box Prompt Tuning With Subspace Learning

Zheng, Y., Tan, Z., Li, P., & Liu, Y. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3407519 · Cited by 14

How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts

Kumarage, T., Sheth, P., Moraffah, R., Garland, J., & Liu, H. (2023) · arXiv · doi:10.48550/arxiv.2310.05095 · Cited by 11

Dialogue acts enhanced extract–abstract framework for meeting summarization

Sun, S., Yuan, R., Li, W., Cao, Z., & Li, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2023.103635 · Cited by 11

NaturalVLM: Leveraging Fine-Grained Natural Language for Affordance-Guided Visual Manipulation

Xu, R., Shen, Y., Li, X., Wu, R., & Dong, H. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3477095 · Cited by 11

Intention Recognition of Space Noncooperative Targets Using Large Language Models

Jing, H., Sun, Q., Dang, Z., & Wang, H. (2025) · Space Science & Technology · doi:10.34133/space.0271 · Cited by 11

A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition

Guan, Z., & Zhou, X. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05172-9 · Cited by 11

A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning

Gu, N., Fu, P., Liu, X., Liu, Z., Lin, Z., & Wang, W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.194 · Cited by 14

What Makes Pre-trained Language Models Better Zero-shot Learners?

Lu, J., Zhu, D., Han, W., Zhao, R., Namee, B. M., & Tan, F. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.128 · Cited by 17

Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?

Sun, J., & Moens, M. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/577 · Cited by 15

SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains

Goswami, K., Lange, L., Araki, J., & Adel, H. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.197 · Cited by 12

Prompt-Based Grouping Transformer for Nucleus Detection and Classification

Huang, J., Li, H., Sun, W., Wan, X., & Li, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_55 · Cited by 11

Adaptive Prompt Routing for Arbitrary Text Style Transfer with Pre-trained Language Models

Liu, Q., Qin, J., Ye, W., Mou, H. L., He, Y., & Wang, K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29832 · Cited by 11

Knowledge-injected prompt learning for actionable information extraction from crisis-related tweets

Gao, W., Zheng, C., Zhu, X., Deng, H., Wang, Y., & Hu, G. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109398 · Cited by 10

Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR Object Recognition

Guo, W., Liv, S., & Yang, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00311 · Cited by 14

A soft prompt learning method for medical text classification with simulated human cognitive capabilities

Wang, Y., Zhou, L., Zhang, W., Zhang, F., & Wang, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11121-0 · Cited by 12

SDD-LawLLM: Advancing Intelligent Legal Systems Through Synthetic Data-Driven Fine-Tuning of Large Language Models

Ma, H., Lu, Y., Xiao, Z., Feng, J., Zhang, H., & Yu, J. J. (2025) · Electronics · doi:10.3390/electronics14040742 · Cited by 12

Efficient and explainable sequential recommendation with language model

Li, Z., Zou, L., Li, C., & Li, C. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104122 · Cited by 11

One Size Doesn’t Fit All: Predicting the Number of Examples for In-Context Learning

Chandra, M., Ganguly, D., & Ounis, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88708-6_5 · Cited by 12

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Feng, R., Pearce, H. A., Liguori, P., & Sui, Y. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3591934 · Cited by 10

Prefix Propagation: Parameter-Efficient Tuning for Long Sequences

Li, J., Aitken, W., Bhambhoria, R. V., & Zhu, X. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.120 · Cited by 15

Parameter-Efficient Legal Domain Adaptation

Li, J., Bhambhoria, R. V., & Zhu, X. (2022) · Natural Legal Language Processing Workshop (NLLP) · doi:10.18653/v1/2022.nllp-1.10 · Cited by 15

À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting

Bowman, B., Achille, A., Zancato, L., Trager, M., Perera, P., Paolini, G., & Soatto, S. (2023) · 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/cvpr52729.2023.01439 · Cited by 14

SMoP: Towards Efficient and Effective Prompt Tuning with Sparse Mixture-of-Prompts

Choi, J., Kim, J., Park, J.-H., Mok, W.-L., & Lee, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.884 · Cited by 12

SeqGPT: An Out-of-the-Box Large Language Model for Open Domain Sequence Understanding

Yu, T., Jiang, C., Lou, C., Huang, S., Wang, X., Liu, W., Cai, J., Li, Y., Li, Y., Tu, K., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29917 · Cited by 15

StoryImager: A Unified and Efficient Framework for Coherent Story Visualization and Completion

Tao, M., Bao, B., Tang, H., Wang, Y., & Xu, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72992-8_27 · Cited by 18

A Few-Shot Approach to Resume Information Extraction via Prompts

Gan, C., & Mori, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35320-8_32 · Cited by 10

Decoding Activist Public Opinion in Decentralized Self-Organized Protests Using LLM

Zhang, B., Chen, T., Wang, X., Li, Q., Zhang, W., & Wang, F. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3398815 · Cited by 9

SLoB: Suboptimal Load Balancing Scheduling in Local Heterogeneous GPU Clusters for Large Language Model Inference

Jiang, P., Wang, H., Cai, Z., Gao, L., Zhang, W., Ma, R., & Zhou, X. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3423749 · Cited by 11

Use of prompt-based learning for code-mixed and code-switched text classification

Udawatta, P., Udayangana, I., Gamage, C., Shekhar, R., & Ranathunga, S. (2024) · World Wide Web · doi:10.1007/s11280-024-01302-2 · Cited by 9

Prompt Tuning for Item Cold-start Recommendation

Jiang, Y., Chen, G., Zhang, W., Wang, J., Jiang, Y., Zhang, Q., Lin, J., Jiang, P., & Bian, K. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688126 · Cited by 9

Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2

Wu, Z., Yang, H., Cai, Y., Yu, B., Liang, C., Duan, Z., & Liang, Q. (2024) · Remote Sensing · doi:10.3390/rs16214056 · Cited by 9

Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text

Zhuang, Y., Zhang, J., Li, X., Liu, C., Yu, Y., Dong, W., & He, K. (2024) · JMIR Medical Informatics · doi:10.2196/63020 · Cited by 10

SIFT: enhance the performance of vulnerability detection by incorporating structural knowledge and multi-task learning

Wang, L., Lu, G., Chen, X., Dai, X., & Qiu, J. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00507-7 · Cited by 9

A Survey on the Scheduling of DL and LLM Training Jobs in GPU Clusters

Fu, T., Yang, Z., Ye, Z., Ma, C., Han, Y., Luo, Y., Wang, X., & Wang, Z. (2025) · Chinese Journal of Electronics · doi:10.23919/cje.2024.00.070 · Cited by 10

GutGPT: A multidimensional knowledge-enhanced large language model for gastrointestinal medicine

Zhang, R.-Y., Qiang, P.-P., Hao, Y.-X., Tan, H., Zhao, K., Cai, L.-J., & Wang, J. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104885 · Cited by 9

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers

Ren, L., Chen, C., Wang, L., & Hua, K. A. (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/cvpr52734.2025.00411 · Cited by 12

RuleMaster+: LLM-Based Automated Rule Generation Framework for Intrusion Detection Systems

Lian, W., Zhang, C., Zhang, H., Jia, B., & Liu, B. (2025) · Chinese Journal of Electronics · doi:10.23919/cje.2024.00.342 · Cited by 9

Degradation-Aware Prompted Transformer for Unified Medical Image Restoration

Wei, J. Y., Yang, G., Wang, Z., Tao, S., Liu, A. L. A., & Chen, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3644795 · Cited by 9

Efficient framework for low-resource abstractive summarization by meta-transfer learning and pointer-generator networks

Huh, T., & Ko, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121029 · Cited by 9

Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting

Lin, R., & Ng, H. T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.324 · Cited by 13

Genetic Prompt Search via Exploiting Language Model Probabilities

Zhao, J., Wang, Z., & Yang, F. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/588 · Cited by 13

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

Tunable Soft Prompts are Messengers in Federated Learning

Dong, C., Xie, Y., Ding, B., Shen, Y., & Li, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.976 · Cited by 18

Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning

Zhou, H., Wan, X., Vulić, I., & Korhonen, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.870 · Cited by 17

Let’s Discover More API Relations: A Large Language Model-Based AI Chain for Unsupervised API Relation Inference

Huang, Q. Y., Sun, Y., Xing, Z., Cao, Y., Chen, J., Xu, X., Jin, H., & Lu, J. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3680469 · Cited by 9

RankGuess: Password Guessing Using Adversarial Ranking

Yang, T., & Wang, D. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00040 · Cited by 9

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

NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., & Cohen, J. (2023) · arXiv · doi:10.48550/arxiv.2310.10501 · Cited by 8

Knowledge interaction graph guided prompting for event causality identification

Hu, R., Li, J., Liu, H., Qi, G., & Zhang, Y. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06095-5 · Cited by 8

Information bottleneck-driven prompt on graphs for unifying downstream few-shot classification tasks

Zhang, X., Chen, W., Cai, F., Zheng, J., Pan, Z., Guo, Y., & Chen, H. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104092 · Cited by 8

A multi-modal sarcasm detection model based on cue learning

Lu, M., Dong, Z., Guo, Z., Zhang, X., Lu, X., Wang, T., & Zhang, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-94266-w · Cited by 8

Robust fine-tuning for low-resource NLP: Combining adversarial and metric-based learning to mitigate overfitting

Choi, K., & Ko, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127737 · Cited by 8

Efficient Knowledge Transfer in Multi-Task Learning through Task-Adaptive Low-Rank Representation

Zhang, X., Wang, K., Hu, T., & Ma, H. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210217 · Cited by 8

Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing

Wang, Y., Xia, C., Wang, G., & Yu, P. S. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020766 · Cited by 8

Few-Shot Composition Learning for Image Retrieval with Prompt Tuning

Wu, J., Wang, R., Zhao, H., Zhang, R., Lu, C., Li, S., & Henao, R. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25597 · Cited by 8

Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning

Liang, Z., Feng, W., Jie, Y., Qian, Y., Hao, Z., & Han, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.16 · Cited by 8

Continued Pretraining for Better Zero- and Few-Shot Promptability

Wu, Z., Logan, R. L., Walsh, P., Bhagia, A., Groeneveld, D., Singh, S., & Beltagy, I. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.300 · Cited by 9

When Whisper Meets TTS: Domain Adaptation Using only Synthetic Speech Data

Vásquez-Correa, J. C., Arzelus, H., Martín-Doñas, J. M., Arellano, J., González-Docasal, A., & Álvarez, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40498-6_20 · Cited by 8

Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning

Dong, X., Zhu, Z., Wang, Z., Teleki, M., & Caverlee, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.390 · Cited by 14

Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts

Engin, D., & Avrithis, Y. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00298 · Cited by 10

Artificial Intelligence-Powered Construction of a Microbial Optimal Growth Temperature Database and Its Impact on Enzyme Optimal Temperature Prediction

Wang, X., Zong, Y., Zhou, X., Xu, L., He, W., & Quan, S. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c06526 · Cited by 8

LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training

Le, K. M., Pham, T., Quan, T., & Luu, A. T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29804 · Cited by 11

Deep Learning Social Network Access Control Model Based on User Preferences

Shan, F., Li, F., Wang, Z., Ji, P., Wang, M., & Sun, H. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.047665 · Cited by 8

Exploring Edge-Driven Collaborative Fine-Tuning Toward Customized AIGC Services

Zhang, Q., Zong, T., Xiong, Z., Sun, P., Zhu, J., Qiu, W., Zheng, Z., & Poor, H. V. (2024) · IEEE Network · doi:10.1109/mnet.2024.3441040 · Cited by 10

PYRA: Parallel Yielding Re-activation for Training-Inference Efficient Task Adaptation

Xiong, Y., Chen, H., Hao, T., Lin, Z., Han, J., Zhang, Y., Wang, G., Bao, Y., & Ding, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72673-6_25 · Cited by 14

FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance

Zhuang, J., Hu, J., Mu, L., Hu, R., Liang, X., Ye, J., & Hu, H. R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72684-2_14 · Cited by 9

Detecting caste and migration hate speech in low-resource Tamil language

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