@inproceedings{limkonchotiwat-etal-2025-wangchanthaiinstruct,
    title = "{W}angchan{T}hai{I}nstruct: An instruction-following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in {T}hai",
    author = "Limkonchotiwat, Peerat  and
      Tuchinda, Pume  and
      Lowphansirikul, Lalita  and
      Nonesung, Surapon  and
      Tasawong, Panuthep  and
      Aji, Alham Fikri  and
      Udomcharoenchaikit, Can  and
      Nutanong, Sarana",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.175/",
    pages = "3535--3558",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models excel at instruction-following in English, but their performance in low-resource languages like Thai remains underexplored. Existing benchmarks often rely on translations, missing cultural and domain-specific nuances needed for real-world use. We present WangchanThaiInstruct, a human-authored Thai dataset for evaluation and instruction tuning, covering four professional domains and seven task types. Created through a multi-stage quality control process with annotators, domain experts, and AI researchers, WangchanThaiInstruct supports two studies: (1) a zero-shot evaluation showing performance gaps on culturally and professionally specific tasks, and (2) an instruction tuning study with ablations isolating the effect of native supervision. Models fine-tuned on WangchanThaiInstruct outperform those using translated data in both in-domain and out-of-domain benchmarks. These findings underscore the need for culturally and professionally grounded instruction data to improve LLM alignment in low-resource, linguistically diverse settings."
}