Robuta

https://deepai.org/publication/lion-implicit-vision-prompt-tuning LION: Implicit Vision Prompt Tuning | DeepAI Mar 17, 2023 - 03/17/23 - Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computatio... prompt tuninglionimplicitvisiondeepai https://arxiv.org/abs/2501.01120 [2501.01120] Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning Abstract page for arXiv paper 2501.01120: Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning prompt tuningretrievalaugmenteddynamic https://aclanthology.org/2022.naacl-main.290/ On Transferability of Prompt Tuning for Natural Language Processing - ACL Anthology Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou.... natural language processingprompt tuningtransferability https://www.simplilearn.com/prompt-tuning-article Understand Prompt Tuning in AI May 3, 2026 - Find benefits of prompt tuning: enhance pre-trained language model performance without altering core architecture by adjusting prompts for improved responses. prompt tuningunderstandai https://openreview.net/forum?id=3uVAA3ckxT ADAPT: Adaptive Prompt Tuning for Vision-Language Models | OpenReview Prompt tuning has emerged as an effective way for parameter-efficient fine-tuning. Conventional deep prompt tuning inserts continuous prompts of a fixed... vision language modelsprompt tuningadaptopenreview https://www.utwente.nl/en/eemcs/dmb/assignments/open/master/Computer%20Vision%20and%20Biometrics/20260106_Visual%20Prompt%20Tuning%20for%20Generalized%20Medical%20Foundation%20Models/ Visual Prompt Tuning for Generalized Medical Foundation Models | Computer Vision and Biometrics |... prompt tuningmedical foundation https://arxiv.org/abs/2210.04831v1 [2210.04831v1] Visual Prompt Tuning for Test-time Domain Adaptation Abstract page for arXiv paper 2210.04831v1: Visual Prompt Tuning for Test-time Domain Adaptation prompt tuningtime domain2210visualtest https://www.sintef.no/en/publications/publication/0198cc798c3e-c929d1eb-8401-4873-a903-39d6ba69096b/ ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER - SINTEF few shot nerprompt tuningcontrastivebasedsintef https://openreview.net/forum?id=MoTUdh9ZCc DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection | OpenReview Vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot capabilities for various downstream tasks. Their performance can be further... prompt tuningwith outrobustdistributiondetection https://openreview.net/forum?id=jDpdQPMosW Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency | OpenReview We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are that prompt tuning... capacity and efficiencyprompt tuningfundamentallimits https://openreview.net/forum?id=2b9aY2NgXE Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning | OpenReview Fine-tuning vision-language models (VLMs) like CLIP to downstream tasks is often necessary to optimize their performance. However, a major obstacle is the... prompt tuningenhancingclipexploring https://aclanthology.org/2022.findings-emnlp.258/ Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation - ACL Anthology Xu Guo, Boyang Li, Han Yu. Findings of the Association for Computational Linguistics: EMNLP 2022. 2022. the sampleprompt tuning https://aclanthology.org/2024.emnlp-main.218/ M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning - ACL Anthology Taowen Wang, Yiyang Liu, James Chenhao Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang,... prompt tuningzero shotmultimodal https://arxiv.org/abs/2406.19486v1 [2406.19486v1] LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models Abstract page for arXiv paper 2406.19486v1: LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models rank prompt https://openreview.net/forum?id=nkHEl4n0JU Visual Fourier Prompt Tuning | OpenReview With the scale of vision Transformer-based models continuing to grow, finetuning these large-scale pretrained models for new tasks has become increasingly... prompt tuningvisualfourieropenreview https://aclanthology.org/2022.findings-acl.8/ Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning - ACL Anthology Jie Zhou, Le Tian, Houjin Yu, Zhou Xiao, Hui Su, Jie Zhou. Findings of the Association for Computational Linguistics: ACL 2022. 2022. few shot learningprompt tuning https://github.com/google-research/prompt-tuning GitHub - google-research/prompt-tuning: Original Implementation of Prompt Tuning from Lester, et... Original Implementation of Prompt Tuning from Lester, et al, 2021 - google-research/prompt-tuning google researchprompt tuningoriginal implementationgithub https://openreview.net/forum?id=E69bPrykNn LLM Alignment Using Soft Prompt Tuning: The Case of Cultural Alignment | OpenReview Large Language Model (LLM) alignment traditionally relies on supervised fine-tuning or alignment frameworks such as Kullback-Leibler (KL) regularization and... the case ofprompt tuningllmalignmentusing https://arxiv.org/abs/2507.06085 [2507.06085] A Survey on Prompt Tuning Abstract page for arXiv paper 2507.06085: A Survey on Prompt Tuning a surveyprompttuning https://huggingface.co/papers/2306.04933 Paper page - InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding Join the discussion on this paper page information theoretic https://murf.ai/blog/prompt-engineering-vs-fine-tuning Fine Tuning vs Prompt Engineering: Which Approach Works Best? fine tuningprompt engineeringvsapproachworks https://openreview.net/forum?id=lZgORA63ew Discrete Latent Features Ablate Adversarial Attack: A Robust Prompt Tuning Framework for VLMs |... While adversarial fine-tuning can enhance the robustness of vision-language models (VLMs), such approaches are computationally expensive. Adversarial prompt... https://arxiv.org/abs/2302.08102v1 [2302.08102v1] Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition Abstract page for arXiv paper 2302.08102v1: Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition https://wandb.ai/sauravmaheshkar/softprompts/reports/A-Brief-Introduction-to-Prompt-Tuning--Vmlldzo3MTkxNTk5 A Brief Introduction to Prompt Tuning Jun 28, 2024 - This article aims to provide a brief overview of Prompt Tuning Method for Language Model Adaptation from the Google Research Lab along with code and... a brief introductionprompttuning https://wandb.ai/ai-team-articles/dspy-prompt-optimization/reports/Advanced-DSPy-prompt-optimization-Tuning-tracking-with-Weave-and-evaluating-with-Ragas--VmlldzoxNjAyNTkwMw Advanced DSPy prompt optimization: Tuning, tracking with Weave, and evaluating with Ragas Mar 31, 2026 - A comprehensive guide to replacing manual prompt engineering with a data driven optimization loop using DSPy, Weights and Biases Weave, and Ragas for financial... prompt optimizationadvanceddspytuning https://www.hostinger.com/in/tutorials/prompt-engineering-vs-fine-tuning Prompt engineering vs. fine-tuning: Key differences Mar 10, 2026 - Large language models (LLMs) can be customized in two main ways: prompt engineering and fine-tuning. The key difference is that prompt engineering modifies prompt engineeringfine tuningvskeydifferences https://arxiv.org/abs/2408.03195v3 [2408.03195v3] RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning Abstract page for arXiv paper 2408.03195v3: RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning reinforcement learning2408reliefempoweredgraph https://openreview.net/forum?id=MDMV2SxCboX Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning |... We analyze settings where performing classification head or prompt tuning on a pretrained language model can provably help downstream task performance. https://aclanthology.org/2024.emnlp-main.597/ Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together - ACL Anthology Dilara Soylu, Christopher Potts, Omar Khattab. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024. https://www.techtarget.com/searchenterpriseai/tip/Prompt-engineering-vs-fine-tuning-Whats-the-difference When to use prompt engineering vs. fine-tuning | TechTarget Explore prompt engineering vs. fine-tuning vs. RAG, and learn when to apply each optimization technique to improve generative AI accuracy and relevance. when to useprompt engineeringfine tuningvstechtarget https://arxiv.org/abs/2302.08102v2 [2302.08102v2] Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition Abstract page for arXiv paper 2302.08102v2: Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition https://openreview.net/forum?id=jzzEHTBFOT&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2024%2FConference%2FAuthors%23your-submissions) C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion |... In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the... https://arxiv.org/abs/2203.16773 [2203.16773] SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for... Abstract page for arXiv paper 2203.16773: SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks https://wandb.ai/a-sh0ts/NeMo_Megatron_PTuning-demo/reports/How-to-Adapt-your-LLM-for-Question-Answering-with-Prompt-Tuning-using-NVIDIA-NeMo-and-Weights-Biases--Vmlldzo1NjA1MjEx How to Adapt your LLM for Question Answering with Prompt-Tuning using NVIDIA NeMo and Weights &... https://www.codecademy.com/article/prompt-engineering-vs-fine-tuning Prompt Engineering vs Fine Tuning: When to Use Each | Codecademy Compare LLM fine-tuning vs prompt engineering for costs, complexity, and when to use each approach for your AI projects. when to useprompt engineeringfine tuningvscodecademy https://www.hostinger.com/ph/tutorials/prompt-engineering-vs-fine-tuning Prompt engineering vs. fine-tuning: Key differences Mar 10, 2026 - Large language models (LLMs) can be customized in two main ways: prompt engineering and fine-tuning. The key difference is that prompt engineering modifies prompt engineeringfine tuningvskeydifferences https://pmc.ncbi.nlm.nih.gov/articles/PMC10408339/ Medical text classification based on the discriminative pre-training model and prompt-tuning - PMC Medical text classification, as a fundamental medical natural language processing task, aims to identify the categories to which a short medical text belongs.... https://aclanthology.org/2025.starsem-1.3/ Injecting Frame Semantics into Large Language Models via Prompt-Based Fine-Tuning - ACL Anthology Shahid Iqbal Rai, Danilo Croce, Roberto Basili. Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025). 2025. https://openreview.net/forum?id=0ndiQEXIcW POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models | OpenReview Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big... fine tuning https://arxiv.org/abs/2405.17898 [2405.17898] FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction Abstract page for arXiv paper 2405.17898: FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction https://deepai.org/publication/prototypical-verbalizer-for-prompt-based-few-shot-tuning Prototypical Verbalizer for Prompt-based Few-shot Tuning | DeepAI Mar 18, 2022 - 03/18/22 - Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-bas... few shotprototypicalverbalizerpromptbased https://openreview.net/forum?id=bJx4iOIOxn Facing the Elephant in the Room: Visual Prompt Tuning or Full finetuning? | OpenReview As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained... the elephantin room https://openreview.net/forum?id=v1WL01lgp8&referrer=%5Bthe%20profile%20of%20D.%20B.%20Emerson%5D(%2Fprofile%3Fid%3D~D._B._Emerson1) Efficient Evaluation of Bias in Large Language Models through Prompt Tuning | OpenReview Prompting large language models (LLMs) has gained substantial popularity as pre-trained LLMs are capable of performing downstream tasks without requiring large... large language models https://openreview.net/forum?id=q0PbfNwLBq Fight Back Against Jailbreaking via Prompt Adversarial Tuning | OpenReview Although Large Language Models (LLMs) have achieved tremendous success in various applications, they are also susceptible to jailbreak attacks. To protect LLMs... fight backjailbreakingviapromptadversarial https://openreview.net/forum?id=2Y93PtAqCl Revisiting the Power of Prompt for Visual Tuning | OpenReview Visual prompt tuning (VPT) is a promising solution incorporating learnable prompt tokens to customize pre-trained models for downstream tasks. However, VPT and... the powerrevisitingpromptvisualtuning https://www.hostinger.com/ca/tutorials/prompt-engineering-vs-fine-tuning Prompt engineering vs fine-tuning: What is the key difference May 20, 2026 - Learn the differences between fine-tuning and prompt engineering in AI. Discover when to use each approach. what is theprompt engineeringfine tuningvskey https://openreview.net/forum?id=sBJNokmYuV Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled... Fine-tuning vision-language models (VLMs) with abundant unlabeled data recently has attracted increasing attention. Existing methods that resort to the... vision language models https://aclanthology.org/2024.emnlp-main.1131/ DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware... Xinglin Lyu, Junhui Li, Yanqing Zhao, Min Zhang, Daimeng Wei, Shimin Tao, Hao Yang, Min Zhang. Proceedings of the 2024 Conference on Empirical Methods in...