⚡FlashRAG: A Python Toolkit for Efficient RAG Research FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre processed benchmark RAG datasets and 16 state of the art RAG algorithms. With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components. For more information, please view our GitHub repo and paper: GitHub repo: https://github.com/RUC NLPIR/FlashRAG/ Paper link: FlashRAG: A Modular Toolkit for Efficient Retrieval Augmented Generation Research. Dataset Card for FlashRAG Datasets We have collected and processed 35 datasets widely used in RAG research, pre processing them to ensure a consistent format for ease of use. For certain datasets (such as Wiki asp), we have adapted them to fit the requirements of RAG tasks according to the methods commonly used within the community. Dataset Details For each dataset, we save each split as a jsonl file, and each line is a dict as follows: Below is the list of datasets along with the corresponding sample sizes: Task Dataset Name Knowledge Source Train Dev…
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