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

Adapting Large Language Models for Automatic Annotation of Radiology Reports for Metastases Detection

Barabadi, M. A., Chan, W. Y., Zhu, X., Simpson, A. L., & Gian, R. K. (2024) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece59415.2024.10667245 · Cited by 1

Automatic Question Answering for the Linguistic Domain – An Evaluation of LLM Knowledge Base Extension with RAG

Lång, C., Schneider, R., & Tu, N. D. T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70242-6_16 · Cited by 1

CP2GFed: Cross-granular and Personalized Prompt-based Green Federated Tuning for Giant Models

Gao, F., Zhao, Y., Qiu, C., Wang, X., Yao, H., & Hu, Q. (2024) · International Workshop on Quality of Service · doi:10.1109/iwqos61813.2024.10682866 · Cited by 1

Utterance-Aware Adaptive Data Labeling and Summarization: Exploiting Large Language Models for Unbiased Dialog Annotation

Glazkov, N., & Makarov, I. (2024) · IEEE Access · doi:10.1109/access.2024.3476981 · Cited by 1

AQLoRA: An Adaptive Quantization-Based Efficient Fine-Tuning Method for LLMs

Huang, X., Huo, Y., Wong, D. F., Wang, Y., Cai, L., & Jiang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9434-8_21 · Cited by 1

LLM4ITD: Insider Threat Detection with Fine-Tuned Large Language Models

Zhang, M., Liang, X., Tian, F., Yang, Y., Yu, H., & Li, B. (2024) · International Conference on Interactive Intelligent Systems and Techniques (IIST) · doi:10.1109/iist62526.2024.00017 · Cited by 3

Encapsulating Knowledge in One Prompt

Li, Q., Yu, R., & Wang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73650-6_13 · Cited by 2

LIPT: Improving Prompt Tuning with Late Inception Reparameterization

He, Y., Feng, A., Gao, Z., & Song, X. (2024) · Electronics · doi:10.3390/electronics13234741 · Cited by 1

On Unified Prompt Tuning for Request Quality Assurance in Public Code Review

Chen, X., Li, L., Zhang, R., & Liang, P. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5569-1_12 · Cited by 1

CoCM: Conditional Cross-Modal Learning for Vision-Language Models

Yang, J., Xie, S., Li, S., Cai, Z., Li, Y., & Zhu, W. (2024) · Electronics · doi:10.3390/electronics14010026 · Cited by 1

Frozen Weights as Prior for Parameter-Efficient Fine-Tuning

Ma, X., Liu, P., Gao, H., Yan, Z., Ma, N., Liu, W., Wang, X., & Tang, R. (2025) · IEEE Access · doi:10.1109/access.2025.3529807 · Cited by 2

Collaborative Framework for Dynamic Knowledge Updating and Transparent Reasoning with Large Language Models

Ding, Z., Ye, P.-G., Wu, Y., & Ren, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1148-5_11 · Cited by 1

Sentop: sentence-level prefix prompt for controllable abstractive summarization

Zhao, S. G., Sun, Y., Chen, J., Duan, Z. Y., Zou, C., Cheng, Y., & He, F. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02669-y · Cited by 1

Enhancing Prompt Tuning with Multitask Embedding Reparameterization

Shi, X., Song, Y., Liu, J., Li, Y., & Sun, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6603-4_11 · Cited by 1

HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation

Gui, A., & Xiao, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.475 · Cited by 4

UMSE: Unified Multi-scenario Summarization Evaluation

Gao, S., Yao, Z., Tao, C., Chen, X., Ren, P., Ren, Z., & Chen, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.236 · Cited by 2

Evaluating Parameter Efficient Learning for Generation

Xu, P., Patwary, M. A., Prabhumoye, S., Adams, V., Prenger, R., Ping, W., Lee, N., Shoeybi, M., & Catanzaro, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.319 · Cited by 1

Improve Performance of Fine-tuning Language Models with Prompting

Yang, Z. G., & Ligeti-Nagy, N. (2023) · Híradástechnika/Infocommunications journal · doi:10.36244/icj.2023.5.10 · Cited by 1

When Do Decompositions Help for Machine Reading?

Wei, K., Lawrie, D., Durme, B. V., Chen, Y., & Weller, O. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.219 · Cited by 2

Assortment of Attention Heads: Accelerating Federated Peft with Head Pruning and Strategic Client SelectionPreprint

Venkatesha, Y., Kundu, S., & Panda, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4790569 · Cited by 1

Adapting Language Models to Compress Contexts

Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · arXiv · Cited by 59

Development and application of a generative-AI-driven virtual simulation teaching system for international business negotiation scenarios

Mo, Q. (2026) · Scientific Reports · doi:10.1038/s41598-026-64132-4

PK-BERT: Knowledge Enhanced Pre-trained Models with Prompt for Few-Shot Learning

Ma, H., Ng, B. K., & Lam, C. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-12127-2_2

PromptFusion: A Low-Cost Prompt-Based Task Composition for Multi-task Learning

Song, H., He, H., Zhu, Q., & Xue, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30105-6_41

Data-efficient domain adaptation for pretrained language models

Guo, X. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/167965

Exploring the Potential of Large Language Models in Personalized Diabetes Treatment Strategies (Preprint)Preprint

Yang, H., Li, J., Liu, S., Liu, L., Liu, X., Huang, Y., Shi, Q., & liu, J. (2023) · doi:10.2196/preprints.50911

HyperT5: Towards Compute-Efficient Korean Language Modeling

Park, D., Ka, S., Yoo, K. M., Lee, G., & Kang, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.40

Improvement on Knowledge-grounded Dialogue Generation with Continuous Prompt Tuning

Ye, H., Xi, Y., & Wang, H. (2023) · IEEE International Conference on Electronics Information and Emergency Communication · doi:10.1109/iceiec58029.2023.10200261

Pre-training Extractive Question-Answer Prompts for Few-Shot Chinese Text Classification

Ding, G., Zheng, S., & Wang, Q. (2023) · Atlantis Highlights in Intelligent Systems/Atlantis highlights in intelligent systems · doi:10.2991/978-94-6463-222-4_34

PTSTEP: Prompt Tuning for Semantic Typing of Event Processes

Zhu, W., Xu, Y., Xu, H., Tang, M., & Zhu, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44213-1_45

Vulnerability Analysis of Continuous Prompts for Pre-trained Language Models

Li, Z., Shi, Y., Sheng, X., Yin, C., Zhou, L. Q., & Li, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44201-8_41

Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefix

Huang, K.-H., Tan, L., Hou, R., Wang, S., Almahairi, A., & Rinott, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.497

SACFormer: Unify Depth Estimation and Completion with Prompt

Tang, S., Wu, D., Wang, Y., & Wang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8432-9_9

Tiny Updater: Towards Efficient Neural Network-Driven Software Updating

Zhang, L., & Ma, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02143 · Cited by 1

What Do European Futures Look Like According to Fine-Tuned Gpt-3, Prompt-Tuned Gpt-3.5, and Few-Shot Prompted Gpt-4?Preprint

Kozachek, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4767192

A Prompt-tuning Method For Low-Resource Relation Extraction Based On Enhanced-Prototype

Xu, H. (2023) · International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE) · doi:10.1109/icemce60359.2023.10490490

Pre-trained Large Model Fine-tuning with Case-based Reasoning Framework for Transportation Risk Scene Prevention

Zhong, W., Huang, J., & Yu, R. (2023) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00028

A Framework for Enhancing Behavioral Science Research with Human-Guided Language Models

Scheuerman, J., & Acklin, D. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31206 · Cited by 1

ProfLLM: A framework for adapting offline large language models to few-shot expert knowledge

Wang, H. N., Zeng, S., Xue, Z., Zhu, X., Wu, F., & Wang, N. (2023) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1145/3652628.3652746

Chinese Open Event Extraction via Low-Rank Adaptation of ChatGLM6b

Bao, C., Lyu, M., Tang, J., Li, S., & Wang, T. (2023) · Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF) · doi:10.1145/3660395.3660461

Multi-Dimensional Training Data Construction Strategies for Text Classification by Exploiting Large Language ModelsPreprint

Lyu, J., Xue, J., Han, W., Chen, J., & Liu, Z. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4879408

CoFF-CHP: coarse-to-fine filters with concept heuristic prompt for few-shot relation classification

Li, P., Hu, S., Zheng, J., Shao, T., Wang, M., & Cai, F. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05572-1

VulPrompt: Prompt-Based Vulnerability Detection Using Few-Shot Graph Learning

Irtiza, S., Li, X., Zamani, M., Khan, L. R., & Hamlen, K. W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65172-4_14

Biulkbqa: A Joint Generate and Retrieve Efficient Dual-Stream Framework for Knowledge Base Question AnsweringPreprint

Chen, Y., Li, Y., & Wen, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4906050

Confidence-Driven Contrastive Learning for Document Classification without Annotated Data

Xu, Z., & Iwaihara, M. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023edp7275

UTPrompt: Cross-Task Backdoor Prompt Attacks Based on Universal Triggers

Jiang, Y., Wang, P., Li, Q., & Liu, N. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5603-2_35

A Trustworthy Service Transaction Framework for Privacy Protection

Li, Z., Mo, T., Li, W., & Tu, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-5760-2_8

Harnessing the Power of Prompt Experts: Efficient Knowledge Distillation for Enhanced Language Understanding

Meng, X., Rao, J., Qi, S., Wang, L., Xiao, J., & Wang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70371-3_13

Knowledge-enhanced Multi-Granularity Interaction Network for Political Perspective Detection

Chen, X., Liu, X., Xu, Y., Wu, B., & Zhu, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650944

A Decomposition Framework for Class Incremental Text Classification with Dual Prompt Tuning

Xu, B., Wu, Y., Li, S., Song, H., Du, M., & Wang, H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650139

Adapting Pre-trained Language Models to Vision-Language Tasksvia Dynamic Visual Prompting

Huang, S., Wu, Q., & Zhou, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651317

ASR Model Adaptation with Domain Prompt Tuning

Zou, P., Ye, J., & Zhou, H. (2024) · International Conference on Asian Language Processing · doi:10.1109/ialp63756.2024.10661142

ADT: An Additive Delta-Tuning approach for parameter-efficient tuning in pre-trained language models

Li, D., Tang, J., Li, S., & Wang, T. (2024) · doi:10.1109/icnlp60986.2024.10692603

Efficient Token Sparsification Through the Lens of Infused Knowledge

Peng, B., Chen, B., He, W. P., Thorne, W., & Kadirkamanathan, V. (2024) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion59988.2024.10706518

L-APPLE: Language-agnostic Prototype Prefix Learning for Cross-lingual Event Detection

Zhu, Z., Peng, X., Li, Q., Ji, C., Sun, Q., & Li, J. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679769

Essay Coherence Evaluation and Feedback Enhanced by Semi-supervised Learning and Auxiliary Information

Li, C., Zhang, L., Guo, H., & Zheng, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9443-0_27

Exploring the Potential of Prompting Methods in Low-Resource Speech Recognition with Whisper

Chen, Y., Zhang, W., Zhang, H., Yang, X., & Qu, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9437-9_30

ViPro-BEV: Few-Shot Visual Prompting for Bird’s-Eye-View Perception

Yuan, G., Huang, H., & Fan, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8792-0_2

Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Tian, P., & Liu, D. (2024) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp62122.2024.10743815

EC-PEFT: An Expertise-Centric Parameter-Efficient Fine-Tuning Framework for Large Language Models

Zhang, Y., Cheng, X., Jiang, Y., & Bei, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0119-6_29

Named Entity Recognition of New Energy Vehicle Parts Based on Large Language Model

Ye, J., Dai, B., Kuang, Z., & Tao, P. (2024) · International Conference on Artificial Intelligence, Automation and Algorithms (AI2A) · doi:10.1145/3700523.3700532

TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models

Adhikari, R., Thapaliya, S., Dhakal, M., & Khanal, B. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0908-6_3 · Cited by 1

Research on Training Scenario Assisted Design Method Based on Domain-Adapted Language Model

Xie, Z., Xu, H., Yang, C., Zhao, L., Li, J., & Liu, A. (2024) · International Conference on New Trends in Computational Intelligence (NTCI) · doi:10.1109/ntci64025.2024.10776221

Multimodal Prompt Learning for Audio Visual Scene-Aware Dialog

Xu, F., Jia, F., & Zhou, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-2064-7_7

C2RG: Parameter-efficient Adaptation of 3D Vision and Language Foundation Model for Coronary CTA Report Generation

Ye, Z., Sun, Y., Shi, W., Yang, B., Wu, S., Wang, H., Xu, C., Zheng, H., Wang, Y., & Zhang, T. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821939

CELL: Supplementing Context for Multi-modal Few-shot Cardiologist

Zhang, H., Shi, J., Ge, Y., Chang, S., Wang, H., & Huang, Q. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822560

Plan, Generate and Optimize: Extending Large Language Models for Dialogue Systems Via Prompt-Based Collaborativec Method

Guo, M., Chen, S., Huang, Y., & Feng, J. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832280

DPCA: Dynamic Probability Calibration Algorithm in LMaaS

Deng, Z., Chen, C. L. P., & Zhang, T. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831099

Construction of Prompt Verbalizer Based on Dynamic Search Tree for Text Classification

Gao, J., Xia, X., Yao, R., Zhang, J., & Zhang, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3532458

SndGLM-SA: A Social Network Debate Text Generation Model Based on Sentiment Analysis

Tian, T. (2024) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc59868.2024.10850019

Unsupervised Statistical Keyword Extraction Pipeline: Is LLM All You Need?

Zagatti, F. R., Lucrédio, D., & Caseli, H. D. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79032-4_32

Tuning Hypothesis Creation: Combining Discrete and Continuous Spaces for Zero-Shot Hate Speech Detection

Vecchi, L. P., Barbon, S., & Paraíso, E. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79038-6_18

Generalized Conditional Similarity Learning via Semantic Matching

Shi, Y., Li, R., Gan, L., Zhan, D., & Ye, H.-J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3535730

A Prompt and Contrastive Learning-Based Model for Plant Antimicrobial Peptide Prediction

Qin, Z., Meng, J., Li, H., Tang, Y., & Luan, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1907-8_14

Effectively Prompting Small-Sized Language Models for Cross-Lingual Tasks via Winning Tickets

Li, M., & Luo, F. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00058

Prompt-augmented Feature with Cross-domain Contrastive Learning for Efficient Multi-domain Sentiment Analysis

Li, R., Yang, T., Liu, C., Jiang, D., Wong, H., & Wu, S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888768

Hierarchical Prompt Tuning 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/icassp49660.2025.10890029

HieRelBERT: Enhanced Lexical Relation Embedding Based-On Hierarchical Contrast Learning

Li, L., Zhang, Y., Zou, J., & Huang, Y. (2025) · Mechanisms and machine science · doi:10.1007/978-3-031-82907-9_60

Hierarchical Prefixes for Long Document Representations

Boucharenc, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88720-8_28

Patch Ranking: Token Pruning as Ranking Prediction for Efficient CLIP

Wu, C.-E. R., Lin, J., Hu, Y. H., & Morgado, P. M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00570 · Cited by 1

Overview of the Typical Case Diagnosis Consistency Evaluation Task in CHIP 2024

Wang, Z., Zong, H., Yang, F., Teng, Z., Yan, J., Jiang, S., & Tang, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4298-4_14

Personalized recommendation with clustering via prompt-tuning

Wu, T., Liu, X., Zhu, Y. Q., Li, Y., Yuan, Y., & Qiang, J. (2025) · Intelligent Data Analysis · doi:10.1177/1088467x251331821

Parameter-Efficient Fine-Tuning of ChatGLM to Mitigate Hallucinations in Chinese Abstractive Summarization

Huang, Y., & Wu, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4001-0_25

CRMR: A Collaborative Multistep Reasoning Framework for Solving Mathematical Problems

Zhang, Y., Tang, X., & Hao, K. (2025) · Machine Intelligence Research · doi:10.1007/s11633-024-1531-3 · Cited by 1

A Benchmark for Multi-Task Evaluation of Pretrained Models in Medical Report Generation

Lin, R., Li, C., & Wang, R. (2025) · BIO Web of Conferences · doi:10.1051/bioconf/202517403010

Multimodal Coupling Prompt Learning for Image Classification Tasks

Liu, Y., Cheng, H. N., Fang, Y., Pan, Y., Qian, Z., & Chen, X. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554251335569

Troubleshooting Detection in Intelligent Customer Service Based on VTRF

Liu, X., Zhang, L., Guan, Y., Zhao, J., Dai, W., & Li, Y. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2528-4_29

TSTBench: A Comprehensive Benchmark for Text Style Transfer

Xie, Y., Gui, J., Che, Z., Zhu, L., Hu, Y., & Pan, Z. (2025) · Entropy · doi:10.3390/e27060575

Negative prompt-guided optimization: Enhancing soft prompt generalization in vision-language models

Kim, S., & Lee, S. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109093

New Objects, New Methods, and New Verifications-Bie-Modernist Cultural Computing on Contemporary South Korean Literary Works

Lin, J., Niu, X., Chen, T., Yin, Z., & Sui, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93163-5_13

APG: Automatic Prompt Generation for Improved Document Summarization

Parnell, J., Unanue, I. J., Matthews, S. L., & Piccardi, M. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3577363

Information-theoretic complementary prompts for improved continual text classification

Zhang, D., Ren, Y. S., Li, C., Yu, D., & Zhang, T. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107676

Memory-Efficient Fine-Tuning for Foundation Models

Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_7

FedDPG: An Adaptive Yet Efficient Prompt-Tuning Approach in Federated Learning Settings

Shakeri, A., Zhang, W. E., Beheshti, A., Chen, W., Yang, J. Y., & Yang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8186-0_4

Boosting LLM’s Continual Sentiment Understanding for Low-Resource Languages

Zhang, H., Wang, J., Luo, J., Zhang, M., & Zhou, G. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3581018 · Cited by 1

Combining Retrieval-Augmented Generation and Fine-tuning of Large Language Models to Enhance Port Industry Question-Answering Systems

Hu, X., & Abisado, M. (2025) · Forum for Linguistic Studies · doi:10.30564/fls.v7i6.9143

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning

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