Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-tuning
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912729925681152 |
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| author | Aung, Thura Kyaw, Eaint Kay Khaing Thu, Ye Kyaw Oo, Thazin Myint Supnithi, Thepchai |
| author_facet | Aung, Thura Kyaw, Eaint Kay Khaing Thu, Ye Kyaw Oo, Thazin Myint Supnithi, Thepchai |
| contents | 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 Fourier-based 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 fastText 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 low-resource language classification. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_21081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-tuning Aung, Thura Kyaw, Eaint Kay Khaing Thu, Ye Kyaw Oo, Thazin Myint Supnithi, Thepchai Computation and Language Artificial Intelligence Machine Learning I.2.7; I.2.6 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 Fourier-based 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 fastText 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 low-resource language classification. |
| title | Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-tuning |
| topic | Computation and Language Artificial Intelligence Machine Learning I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2511.21081 |