@inproceedings{nguyen-etal-2026-accuraterag,
    title = "{A}ccurate{RAG}: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications",
    author = "Nguyen, Linh The  and
      Tran, Chi  and
      Nguyen, Dung Ngoc  and
      Pham, Van-Cuong  and
      Ngo, Hoang  and
      Nguyen, Dat Quoc",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-1.394/",
    doi = "10.63317/2ygvnkbv24j6",
    pages = "5015--5023",
    abstract = "We introduce AccurateRAG{---}a novel framework for constructing high-performance question-answering applications based on retrieval-augmented generation (RAG). Our framework offers a pipeline for development efficiency with tools for raw dataset processing, fine-tuning data generation, text embedding {\&} LLM fine-tuning, output evaluation, and building RAG systems locally. Experimental results show that our framework outperforms previous strong baselines and obtains new state-of-the-art question-answering performance on benchmark datasets."
}