R$^{2}$Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
- Source
- arxiv.org
- Author
- Yucan Guo, Miao Su, Saiping Guan, Long Bai, Zhongni Hou, Zixuan Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
- Date

Context
A question-answering system need not use its most elaborate search method for every question. R²Adapter chooses between retrieving text directly and searching a graph of relationships, which can help when an answer requires connecting several facts. It also rewrites uncertain graph-bound questions to make those connections easier to retrieve. Across three question-answering benchmarks requiring linked reasoning, the authors report up to 59% less use of graph-based retrieval while maintaining comparable answer accuracy. This supports routing questions according to their needs; the reported reduction measures use of the graph pathway, rather than an equivalent percentage reduction in total cost or response time. Study abstract.
- RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
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articleRetrieval-Augmented Generation for Scientific Code UnderstandingAaron Nobile, Andreas Adelmann, Mohsen Sadr
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