AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
This addresses the need for efficient and accurate RAG-based QA systems, but it appears incremental as it builds on existing RAG methods with a new framework.
The paper tackles the problem of building high-performance question-answering applications using retrieval-augmented generation (RAG), and the result is a framework that outperforms previous baselines and achieves new state-of-the-art performance on benchmark datasets.
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.