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Author: search.filters.author.Netisopakul, PonrudeeSubject: search.filters.subject.Large Language Models

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    Benchmarking Next-Generation Frontier Models for Document-Level Arabic-Thai Medical Translation: A Reliability Study of LLM-as-a-Judge
    (2026-01-01)
    Lertsuksakda, Rathawut
    ;
    Netisopakul, Ponrudee
    ;
    Boonsang, Siridech
    This research targets Arabic-to-Thai medical translation, a low-resource and linguistically distant pair critical for public health. We utilize the OpenWHO dataset, a resource comprising 293 documents. This dataset is shielded from webcrawling to minimize training data contamination and ensure rigorous zero-shot evaluation. We conduct a comparative analysis between 2026-era frontier models including GPT-5.2, Gemini 3 Pro, and Claude Opus 4.5 and industry-standard NMT represented by Google Translate. Using an LLM-as-a-Judge framework with an AI jury of efficient reasoning models such as GPT-5.1, Gemini 3 Flash, and Claude Sonnet 4.5, we performed 3,516 evaluations of fidelity, fluency, and cultural appropriateness. Results demonstrate that frontier models outperform traditional NMT across all dimensions. While high exact agreement suggests LLM-as-a-Judge frameworks are promising for scalable evaluation, human validation reveals persistent opportunities for improving AI-human alignment, necessitating targeted oversight for safety-critical applications.
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    Application of Large Language Models for Aspect-Based Sentiment Analysis on Social Media Data: A Case Study of the Thai Telecommunications Industry
    (2026-01-01)
    Limseesawan, Krittapas
    ;
    Netisopakul, Ponrudee
    ;
    Chotipant, Supannada
    ;
    Voravuthikunchai, Winn
    ;
    Sirivorachodphokin, Thitirat
    Social media is a valuable source of consumer opinion data, particularly in Thailand's highly competitive telecommunications market among AIS, TRUE, and DTAC. This study compares three LLM-based approaches for Aspect-Based Sentiment Analysis (ABSA) on 3,508 Thai-language messages from Pantip.com and YouTube.com: (1) Prompt Engineering, (2) Domain Adaptation & Fine-Tuning, and (3) Multi-Agent Debate Framework. Results show that Domain Adaptation & Fine-Tuning achieves the best accuracy-latency trade-off, with Qwen2.5-1.5B-Instruct exceeding 87% average accuracy in under 4 seconds per message. Multi-Agent Debate achieves the highest Category accuracy (82.82%) but at a latency cost of 10-16 seconds per message. Gemini-2.5-Flash-Lite provides the best speed-accuracy balance for Prompt Engineering without additional training. Critically, small open-source models (1B-1.5B parameters) subjected to domain-specific fine-tuning can approach or surpass proprietary large models on this task, suggesting domain alignment may outweigh raw parameter scale for narrow, well-defined ABSA tasks.
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