Prompt Tuning

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Overview

Prompt tuning studies how small learned vectors, inserted into a model’s inputs or internal layers, can adapt a largely frozen pretrained model to new tasks. Common approaches include soft prompts and prefixes, structured or task-specific prompt banks, and routing or test-time methods that select or adjust prompts for each input or domain. These techniques are used with language and vision-language models as well as audio, graphs, and time series, often aiming to reduce training and memory costs while retaining pretrained capabilities. A central challenge is balancing effective adaptation with robustness and generalization: prompts can overfit limited or noisy data, interfere with existing behavior, or behave unpredictably across shifts, and their internal effects are not always well understood.

Latest papers

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76 papers10 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

  1. Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization

    Mar 29, 2026Canran Xiao, Liwei HouRecommender SystemsUser Preference Modeling

    Shenzhen Campus of Sun Yat-sen University Shenzhen, Guangdong, China · Hunan University Changsha, Hunan, China

  2. Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning

    Mar 23, 2026Ulugbek Shernazarov, Rostislav Svitsov, Bin ShiParameter-Efficient Fine-TuningText Summarization

    Telecom SudParis, Institut Polytechnique de Paris, France

  3. Hierarchical Text-Guided Brain Tumor Segmentation via Sub-Region-Aware Prompts

    Mar 22, 2026Bahram Mohammadi, Ta Duc Huy, Afrouz Sheikholeslami +8Medical Image SegmentationBrain Tumor Segmentation

    Macquarie University, Sydney, NSW, Australia · Adelaide University, Adelaide, SA, Australia · Flinders University, Adelaide, SA, Australia +1

  4. A Creative Agent is Worth a 64-Token Template

    Mar 18, 2026Ruixiao Shi, Fu Feng, Yucheng Xie +3T2I GenerationImage Generation

    School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China

  5. Prompt-tuning with Attribute Guidance for Low-resource Entity Matching

    Mar 17, 2026Lihui Liu, Carl YangEntity ResolutionPrompt Tuning

    Wayne State University, Detroit, MI, USA · Emory University, Atlanta, GA, USA

  6. FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models

    Mar 9, 2026Haoyang Li, Liang Wang, Siyu Zhou +5Attention-Guided DistillationVLM Adaptation

    University of Technology Sydney, NSW 2007, Australia · Shanghai University, Shanghai 200444, China

  7. Any to Full: Prompting Depth Anything for Depth Completion in One Stage

    Mar 5, 2026Zhiyuan Zhou, Ruofeng Liu, Taichi Liu +4Depth CompletionPrompt Tuning

    Rutgers University, USA · Michigan State University, USA · JD Logistics, China +1

  8. Position: Vector Prompt Interfaces Should Be Exposed to Enable Customization of Large Language Models

    Mar 4, 2026Liangwei Yang, Shiyu Wang, Haolin Chen +12LLM PromptingPrompt Tuning

    Salesforce AI Research, Palo Alto, CA, United States.

  9. An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code

    Mar 3, 2026Sophie Weidmann, Fernando CastorCode GenerationLLM Code Generation

    University of Twente, Enschede, The Netherlands

  10. CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment

    Mar 3, 2026Maoyuan Shao, Yutong Gao, Xinyang Huang +3VLM AdaptationVision-Language Alignment

    School of Information Engineering, Minzu University of China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications · National Library of China, Beijing, China +1

  11. DeAR: Fine-Grained VLM Adaptation by Decomposing Attention Head Roles

    Mar 1, 2026Yiming Ma, Hongkun Yang, Lionel Z. Wang +3VLM AdaptationPrompt Tuning

    Chongqing Research Institute of Harbin Institute of Technology, China · Ocean University of China, China · The Hong Kong Polytechnic University, Hong Kong SAR +1

  12. Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space

    Feb 27, 2026Jiasen Zheng, Zijun Zhou, Huajun Zhang +3Semantic AlignmentFew-Shot Text Classification

    Northwestern University, Evanston, USA · Independent Researcher, San Jose, USA · Syracuse University, Syracuse, USA +3

  13. CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation

    Feb 23, 2026Mainak Singha, Sarthak Mehrotra, Paolo Casari +3Unsupervised Domain AdaptationFew-Shot Domain Adaptation

    University of Trento, Italy · MDSR Labs Adobe, India · IIT Bombay, India +1

  14. Optimizing Soft Prompt Tuning via Structural Evolution

    Feb 18, 2026Zhenzhen Huang, Chaoning Zhang, Haoyu Bian +8Prompt TuningParameter-Efficient Fine-Tuning

    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China · School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong, China +3

  15. VeriGRAG: Enhancing LLM-Based Verilog Code Generation with Structure-Aware Soft Prompts

    Sep 27, 2025Jiayu Zhao, Zehua Pei, Zetao Guo +2RTL Code GenerationLLM Code Generation

    University of Science and Technology of China

  16. Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models

    Jun 12, 2025Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan +2Diffusion Model AlignmentPrompt Tuning

    School of Engineering, University of Edinburgh · Canon Medical Research Europe Ltd.