@inproceedings{nguyen-quan-2026-works,
    title = "Which Works Best for {V}ietnamese? A Practical Study of Information Retrieval Methods across Domains",
    author = "Nguyen, Long S. T.  and
      Quan, Tho",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.110/",
    pages = "2098--2119",
    ISBN = "979-8-89176-386-9",
    abstract = "Large Language Models (LLMs) have achieved remarkable progress, yet their reliance on parametric knowledge often leads to hallucinations. Retrieval-Augmented Generation (RAG) mitigates this issue by grounding outputs in external documents, where the quality of retrieval is critical. While retrieval methods have been widely benchmarked in English, it remains unclear which approaches are most effective for Vietnamese, a language characterized by informal queries, noisy documents, and limited resources. Prior studies are restricted to clean datasets or narrow domains, leaving fragmented insights. To the best of our knowledge, we present the first comprehensive benchmark of retrieval methods for Vietnamese across multiple real-world domains. We systematically compare lexical, dense, and hybrid methods on datasets spanning education, legal, healthcare, customer support, lifestyle, and Wikipedia, and introduce two new datasets capturing authentic educational counseling and customer service interactions. Beyond reporting benchmark numbers, we distill a set of empirical insights that clarify trade-offs, highlight domain-specific challenges, and provide practical guidance for building robust Vietnamese QA systems. Together, these contributions offer the first large-scale, practice-oriented perspective on Vietnamese retrieval and inform both academic research and real-world deployment in low-resource languages. All datasets and evaluation scripts are available at https://github.com/longstnguyen/ViRE."
}