DECSELFMASK: Leveraging Unlabeled Text via Self-Relevance-Guided Masking for Decoder-Only Classification
arXiv:2606.09466v2 Announce Type: replace Abstract: Classification tasks require annotated data, which can often be expensive, time-consuming, or even unfeasible to collect. This is the case of the medical domain, where large datasets often have few annotated examples. To address this, we propose DecSelfMask (Decoder Self-learning by Masking), an approach to enhance decoder-only performance on classification tasks. We build on common self-learning approaches by leveraging a model to create training examples from unlabeled data to propose a novel relevance-guided masking strategy. We use releva
The increasing demand for specialized AI applications, particularly in fields with limited annotated data like medicine, is driving innovation in self-supervised learning techniques.
This development allows for more efficient and robust classification models with less reliance on costly and time-consuming human-annotated datasets, accelerating AI deployment in critical sectors.
The barrier to entry for developing high-performing AI models in data-scarce domains is lowered, making AI more accessible and enabling new applications in fields like medical diagnosis.
- · AI researchers
- · Healthcare sector
- · Companies with limited proprietary datasets
- · AI platform providers
- · Data labeling services (for certain tasks)
- · Traditional supervised learning approaches
Improved performance of decoder-only models for classification tasks with less labeled data.
Faster development and deployment of AI solutions in highly specialized, data-constrained industries.
Reduced costs for AI development leading to broader AI adoption and potentially novel applications across various sectors.
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