{"ID":2861615,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02243","arxiv_id":"2510.02243","title":"AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications","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 \u0026 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.","short_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 \u0026 LLM fine-tuning,...","url_abs":"https://arxiv.org/abs/2510.02243","url_pdf":"https://arxiv.org/pdf/2510.02243v2","authors":"[\"Linh The Nguyen\",\"Chi Tran\",\"Dung Ngoc Nguyen\",\"Van-Cuong Pham\",\"Hoang Ngo\",\"Dat Quoc Nguyen\"]","published":"2025-10-02T17:30:08Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}