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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.

Terms in this piece · Glossary
  • 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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