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：我们介绍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。该框架为提升开发效率提供了一套完整流程，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（LLM）微调、输出评估以及本地构建RAG系统的工具。实验结果表明，我们的框架超越了先前强大的基线方法，并在基准数据集上取得了新的最先进的问答性能。