Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning

TL;DR

This paper introduces an online update method for the RAG model that uses dynamic memory, hierarchical retrieval, and multi-stage networks to improve knowledge updating and inference accuracy in real-time.

Contribution

It presents a novel online update framework for RAG models incorporating dynamic memory, hierarchical retrieval, and multi-stage generation, enhancing knowledge retention and accuracy.

Findings

Outperforms existing models in knowledge retention

Achieves higher inference accuracy

Effective integration of new and old knowledge

Abstract

In the contemporary context of rapid advancements in information technology and the exponential growth of data volume, language models are confronted with significant challenges in effectively navigating the dynamic and ever-evolving information landscape to update and adapt to novel knowledge in real time. In this work, an online update method is proposed, which is based on the existing Retrieval Enhanced Generation (RAG) model with multiple innovation mechanisms. Firstly, the dynamic memory is used to capture the emerging data samples, and then gradually integrate them into the core model through a tunable knowledge distillation strategy. At the same time, hierarchical indexing and multi-layer gating mechanism are introduced into the retrieval module to ensure that the retrieved content is more targeted and accurate. Finally, a multi-stage network structure is established for different types of inputs in the generation stage, and cross-attention matching and screening are carried out on the intermediate representations of each stage to ensure the effective integration and iterative update of new and old knowledge. Experimental results show that the proposed method is better than the existing mainstream comparison models in terms of knowledge retention and inference accuracy.