构建更准的问答系统，让AI检索生成更可靠。
AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
- 提供从数据处理到系统部署的全流程工具链。
- 在多个基准测试中超越现有方法，达最新性能纪录。
- 适合需要高精度问答的开发者快速搭建应用。
我们提出 AccurateRAG——一个基于检索增强生成（RAG）的高性能问答应用构建框架。该框架提供高效开发流水线，涵盖原始数据处理、微调数据生成、文本嵌入与大模型微调、输出评估以及本地化RAG系统构建等工具。实验结果表明，该框架在多个基准数据集上优于先前强基线方法，实现了新的问答性能纪录。
原文摘要 · Abstract (English)
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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