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Found 2 papers, 1 papers with code
Naive Bayes-based Context Extension for Large Language Models
However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples.
ZLPR: A Novel Loss for Multi-label Classification
To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper.
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