AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications

Linh The Nguyen; Chi Tran; Dung Ngoc Nguyen; Van-Cuong Pham; Hoang Ngo; Dat Quoc Nguyen

doi:10.63317/2ygvnkbv24j6

AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications

Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Van-Cuong Pham, Hoang Ngo, Dat Quoc Nguyen

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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.

Anthology ID:: 2026.lrec-1.394
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 5015–5023
Language:
External URL:: https://lrec.elra.info/lrec2026-main-394
DOI:: 10.63317/2ygvnkbv24j6
Bibkey:
Cite (ACL):: Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Van-Cuong Pham, Hoang Ngo, and Dat Quoc Nguyen. 2026. AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5015–5023, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications (Nguyen et al., LREC 2026)
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@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://aclanthology.org/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."
}

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%0 Conference Proceedings
%T AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
%A Nguyen, Linh The
%A Tran, Chi
%A Nguyen, Dung Ngoc
%A Pham, Van-Cuong
%A Ngo, Hoang
%A Nguyen, Dat Quoc
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F nguyen-etal-2026-accuraterag
%X 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.
%R 10.63317/2ygvnkbv24j6
%U https://aclanthology.org/2026.lrec-1.394/
%U https://doi.org/10.63317/2ygvnkbv24j6
%P 5015-5023

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Markdown (Informal)

[AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications](https://aclanthology.org/2026.lrec-1.394/) (Nguyen et al., LREC 2026)

AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications (Nguyen et al., LREC 2026)

ACL

Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Van-Cuong Pham, Hoang Ngo, and Dat Quoc Nguyen. 2026. AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5015–5023, Palma de Mallorca, Spain. ELRA Language Resource Association.