AccurateRAG：构建准确检索增强型问答应用的框架
AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
AI总结：
AccurateRAG通过高效流程和本地构建能力，在问答任务中实现更高性能和新状态的SOTA表现。
摘要：
我们介绍了AccurateRAG——一种用于构建高性能问答应用的新框架，基于检索增强生成（RAG）。我们的框架提供了一条开发效率的流水线，包含原始数据集处理、微调数据生成、文本嵌入及LLM微调、输出评估以及本地构建RAG系统工具。实验结果表明，我们的框架优于之前的强基线，并在基准数据集上取得了新的问答性能记录。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
