Title: EchoRAG: A Cognitive Memory-Inspired Framework for RAG with Semantic Gist
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Retrieval-augmented generation, Large language models

Abstract: Retrieval-Augmented Generation (RAG), a pivotal technology connecting external knowledge with large language models, has been widely applied in various knowledge-intensive tasks. However, due to the inherent discrete representation of textual information and retrieval paradigms in current mainstream RAG systems, there is a prevalent issue of lack of semantic integrity, which leads to deviations in semantic retrieval. Therefore, we propose the concept of semantic gist and design EchoRAG, a novel RAG framework that simulates human cognitive memory. Specifically, inspired by the human episodic memory mechanism, this framework first achieves an understanding of semantic gist through reasoning and uses this to construct a multi-dimensional knowledge graph. During retrieval, it relies on a thought diffusion module to conduct global thinking on the knowledge graph, building a more comprehensive semantic landscape. Additionally, we propose the CogniRank algorithm, which incorporates the structural relevance of entity nodes and entity frequency metrics to measure node importance, thereby completing efficient ranking of candidate knowledge. To verify the effectiveness of EchoRAG, experiments were conducted on 5 public datasets for Question Answering (QA) tasks and multi-hop reasoning tasks. The results show that compared with current mainstream RAG methods, EchoRAG significantly improves answer accuracy and recall metrics while enhancing speed.