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Author: search.filters.author.Kyaw, Eaint Kay KhaingSubject: search.filters.subject.EfficientKAN

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    Item type:Publication,
    Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-Tuning
    (2025-01-01)
    Aung, Thura
    ;
    Kyaw, Eaint Kay Khaing
    ;
    Thu, Ye Kyaw
    ;
    Oo, Thazin Myint
    ;
    Supnithi, Thepchai
    In low-resource languages like Burmese, classification tasks often fine-tune only the final classification layer, keeping pre-trained encoder weights frozen. While Multi-Layer Perceptrons (MLPs) are commonly used, their fixed non-linearity can limit expressiveness and increase computational cost. This work explores Kolmogorov-Arnold Networks (KANs) as alternative classification heads, evaluating Fourierbased FourierKAN, Spline-based EfficientKAN, and Grid-based FasterKAN-across diverse embeddings including TF-IDF, fastText, and multilingual transformers (mBERT, Distil-mBERT). Experimental results show that KAN-based heads are competitive with or superior to MLPs. EfficientKAN with fast-Text achieved the highest F1-score (0.928), while FasterKAN offered the best trade-off between speed and accuracy. On transformer embeddings, EfficientKAN matched or slightly outperformed MLPs with mBERT (0.917 F1). These findings highlight KANs as expressive, efficient alternatives to MLPs for lowresource language classification.