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Author: search.filters.author.Kyaw, Eaint Kay Khaing

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    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.
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    myFoodQA: A Multimodal Dataset for Evaluating Cultural and Visual Reasoning in Myanmar Gastronomy
    (2025-01-01)
    Phyo, Shin Thant
    ;
    Linn, Pyae
    ;
    Bhone, Lynn Myat
    ;
    Khin, Thet Hmue
    ;
    Kyaw, Eaint Kay Khaing
    This paper introduces myFoodQA (Myanmar (Burmese) Food Question Answering), the first multimodal benchmark designed to evaluate AI models on Myanmar's rich gastronomic culture. A core contribution of this work is the thorough construction of the benchmark itself, which involved curating a diverse set of food images for 20 distinct dishes and, crucially, generating a novel corpus of 2,485 question-answer pairs. The benchmark features tasks for single-image, multi-image, and text-only reasoning, specifically designed to evaluate model understanding of ingredient recognition, cultural context, preparation methods, and comparative logic. To ensure authenticity, data was sourced and collected from personal photography and web-crawling, with all annotations, prompts and questions validated by native Burmese speakers. Leading vision-language models were evaluated in a zero-shot condition and revealed a large performance disparity. While models perform well on text-based tasks, the performance significantly deficit on image-based reasoning, which needs specific image understanding and extensive cultural knowledge. These findings reveal the limitations of current Large Language and Vision Models (LLMs and VLMs) regarding the Myanmar gastronomic domain. Consequently, this work establishes myFoodQA as a foundational resource for advancing multimodal AI in culturally relevant and low-resource settings.