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Author: search.filters.author.Chalothorn, TawunratStart date: 2020

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    Item type:Publication,
    Enhancing HR Support in a Thai Organization with LLM-Based Question Answering
    (2026-01-01)
    Taemkaeo, Chinnatip
    ;
    Saetia, Chanatip
    ;
    Chalothorn, Tawunrat
    ;
    Titijaroonroj, Taravichet
    Large language models (LLMs) enhanced with retrieval-augmented generation (RAG) and multimodal inputs are increasingly used as interfaces to organizational knowledge. However, their effectiveness in specialized, non-English enterprise settings-such as Thai HR support-remains largely unclear. In many Thai organizations, employees frequently ask detailed HR-related questions, but the relevant information is scattered across internal webpages, PDF manuals, announcements, and images, making it difficult for generic LLMs to provide accurate, policy-consistent responses. To address this issue, we develop a multimodal RAG pipeline that combines hybrid dense-sparse retrieval over a vector database and evaluate six LLM models on a private Thai Visual Question Answering (VQA) HR dataset consisting of 226 questions and reference images across five HR topics. The results show that recent multimodal models, especially Qwen2.5-VL, achieve the best performance, with the highest averages in correctness (0.54), relevance (0.75), and helpfulness (0.64), clearly outperforming older vision-language systems and a text-only reasoning model. For large-scale answer evaluation, we apply an LLM-as-a-judge approach using GPT-4.1 and Gemini 2.5 Flash. We found that it serves as a generally reliable, though imperfect, substitute for human evaluation.
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    Survey of Query correction for Thai business-oriented information retrieval
    (2020-11-18)
    Kittiworapanya, Phongsathorn
    ;
    Saelek, Nuttapong
    ;
    Lertpiya, Anuruth
    ;
    Chalothorn, Tawunrat
    The importance of effective Thai information retrieval (IR) increases as more businesses in Thailand undergo digital transformation. However, previous research on Thai IR systems has mainly focused on web search engines. This study will focus on using query correction to reduce user errors to improve Thai IR. Experiments are conducted on our business-oriented Thai IR task (bTIR). Our investigation presented three notable findings. First, cognitive errors are less of an issue in a business setting. Thus, homophones correction methods provide very little to no benefit for bTIR. Second, approximation based spelling correction methods can significantly reduce search performance. Thus, partial matching on a full dictionary, such as symmetric delete indexing (SymSpell), should be preferred over non-optimal search methods. Third, we introduce a re-ranking algorithm for query corrector, which features multiple sub-correctors (e.g., ThaiQCor 2.0), which results in better performance across multiple configurations.