Retrieval augmented models show promise in enhancing traditional language
models by improving their contextual understanding, integrating private data,
and reducing hallucination. However, the processing time required for retrieval
augmented large language models poses a challenge when applying them to tasks
that require real-time responses, such as composition assistance.
To overcome this limitation, we propose the Hybrid Retrieval-Augmented
Generation (HybridRAG) framework that leverages a hybrid setting that combines
both client and cloud models. HybridRAG incorporates retrieval-augmented memory
generated asynchronously by a Large Language Model (LLM) in the cloud. By
integrating this retrieval augmented memory, the client model acquires the
capability to generate highly effective responses, benefiting from the LLM's
capabilities. Furthermore, through asynchronous memory integration, the client
model is capable of delivering real-time responses to user requests without the
need to wait for memory synchronization from the cloud. Our experiments on
Wikitext and Pile subsets show that HybridRAG achieves lower latency than a
cloud-based retrieval-augmented LLM, while outperforming client-only models in
utility.

利用混合检索增强生成（HybridRAG）框架，在需要实时响应的任务（如作文帮助）中，将客户端模型与云端模型相结合，通过异步生成的检索增强记忆使客户端模型具备生成高效响应的能力，并实现实时响应无需等待云端内存同步，从而克服了检索增强大型语言模型处理时间长的局限性。实验证明，HybridRAG 在延迟方面优于基于云的检索增强大型语言模型，并在效用方面优于仅具备客户端的模型。