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Poster
POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models
Korawat Tanwisuth · Shujian Zhang · Huangjie Zheng · Pengcheng He · Mingyuan Zhou

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #100
in Poster Session 1 »
Event URL: https://github.com/korawat-tanwisuth/POUF »

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. PyTorch code is available at https://github.com/korawat-tanwisuth/POUF.

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Author Information

Korawat Tanwisuth (The University of Texas at Austin)
Shujian Zhang (UT Austin)
Huangjie Zheng (The University of Texas at Austin)
Pengcheng He (Microsoft)
Mingyuan Zhou (University of Texas at Austin)

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