In order to thrive in hostile and ever-changing natural environments,
mammalian brains evolved to store large amounts of knowledge about the world
and continually integrate new information while avoiding catastrophic
forgetting. Despite the impressive accomplishments, large language models
(LLMs), even with retrieval-augmented generation (RAG), still struggle to
efficiently and effectively integrate a large amount of new experiences after
pre-training. In this work, we introduce HippoRAG, a novel retrieval framework
inspired by the hippocampal indexing theory of human long-term memory to enable
deeper and more efficient knowledge integration over new experiences. HippoRAG
synergistically orchestrates LLMs, knowledge graphs, and the Personalized
PageRank algorithm to mimic the different roles of neocortex and hippocampus in
human memory. We compare HippoRAG with existing RAG methods on multi-hop
question answering and show that our method outperforms the state-of-the-art
methods remarkably, by up to 20%. Single-step retrieval with HippoRAG achieves
comparable or better performance than iterative retrieval like IRCoT while
being 10-30 times cheaper and 6-13 times faster, and integrating HippoRAG into
IRCoT brings further substantial gains. Finally, we show that our method can
tackle new types of scenarios that are out of reach of existing methods. Code
and data are available at this https URL

本研究介绍了一种灵感来源于人类长期记忆的大脑索引理论的新型检索框架 HippoRAG，通过协同作用 LLMs、知识图谱和 Personalized PageRank 算法模拟新经历的深度高效知识整合。与 RAG 方法进行多跳问答比较后表明 HippoRAG 方法表现出色，并超过先进方法 20%。与 IRCoT 相比，HippoRAG 的单步检索具有可比或更好的性能，并且成本更低、速度更快。此外，将 HippoRAG 引入 IRCoT 可以进一步获得显著的收益，同时展示了该方法可以处理现有方法无法解决的新式场景。