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.

翻译：本文介绍AccurateRAG——一种基于检索增强生成（RAG）构建高性能问答应用的新型框架。该框架提供标准化开发流程以提升开发效率，包含原始数据集处理、微调数据生成、文本嵌入与大型语言模型微调、输出评估及本地RAG系统构建等工具链。实验结果表明，本框架在基准数据集上超越了现有强基线方法，取得了新的最先进问答性能。