mlsanityData to 7 Oct 2026

POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models

arXiv 2305.00350 ↗First posted 29 Apr 2023cs.LG cs.AI cs.CLcited by 29

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Abstract (arXiv)

Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot capabilities, in general, labeled data are still required to adapt them to downstream tasks. To overcome this critical limitation, we propose an unsupervised fine-tuning framework to directly fine-tune the model or prompt on the unlabeled target data. We demonstrate how to apply our method to both language-augmented vision and masked-language models by aligning the discrete distributions extracted from the prompts and target data. To verify our approach's applicability, we conduct extensive experiments on image classification, sentiment analysis, and natural language inference tasks. Across 13 image-related tasks and 15 language-related ones, the proposed approach achieves consistent improvements over the baselines.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 56 of its 68 references that are in the map. First posted on arXiv 29 Apr 2023.

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56 of 68 references.

References · 56

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38 more
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Citing papers per year: 2023: 6; 2024: 11; 2025: 9; 2026: 3.

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  1. 2023In-Context Learning Unlocked for Diffusion Modelscited by 103
  2. 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptationcited by 51
  3. 2024Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Promptscited by 36
  4. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  5. 2023ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptationcited by 34
  6. 2023Tuning Multi-mode Token-level Prompt Alignment across Modalitiescited by 26
  7. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  8. 2024Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Modelscited by 22
  9. 2023PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classificationcited by 21
  10. 2024Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Trainingcited by 15
  11. 2025CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Surveycited by 15
  12. 2024DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detectioncited by 11
17 more
  1. 2023Prototypes-oriented Transductive Few-shot Learning with Conditional Transportcited by 8
  2. 2024AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Modelscited by 6
  3. 2024TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learningcited by 6
  4. 2025Generalizing vision-language models to novel domains: A comprehensive surveycited by 6
  5. 2024Training-Free Unsupervised Prompt for Vision-Language Modelscited by 5
  6. 2024DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Modelscited by 5
  7. 2025ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Modelcited by 2
  8. 2025Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Scorecited by 1
  9. 2025Generalizing Vision-Language Models with Dedicated Prompt Guidancecited by 1
  10. 2026Let's Roll a BiFTA: Bi-refinement for Fine-grained Text-visual Alignment in Vision-Language Modelscited by 1
  11. 2023Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Modelscited by 0
  12. 2025Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learningcited by 0
  13. 2025microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classificationcited by 0
  14. 2025Cooperative Pseudo Labeling for Unsupervised Federated Classificationcited by 0
  15. 2025LGCA: Enhancing Semantic Representation via Progressive Expansioncited by 0
  16. 2026Dual-Stream Semantic Guidance with Prototype Anchor Calibration for Source-Fully-Free Adaptation of Vision-Language Modelscited by 0
  17. 2026Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuningcited by 0