title: MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge
    Discovery
summary: Retrieval-Augmented Generation (RAG) leverages retrieval tools to access
  external databases, thereby enhancing the generation quality of large language
  models (LLMs) through optimized context. However, the existing retrieval
  methods are constrained inherently, as they can only perform relevance matching
  between explicitly stated queries and well-formed knowledge, but unable to
  handle tasks involving ambiguous information needs or unstructured knowledge.
  Consequently, existing RAG systems are primarily effective for straightforward
  question-answering tasks. In this work, we propose MemoRAG, a novel
  retrieval-augmented generation paradigm empowered by long-term memory. MemoRAG
  adopts a dual-system architecture. On the one hand, it employs a light
  but long-range LLM to form the global memory of database. Once a task is
  presented, it generates draft answers, cluing the retrieval tools to locate
  useful information within the database. On the other hand, it leverages an
  expensive but expressive LLM, which generates the ultimate answer
  based on the retrieved information. Building on this general framework, we
  further optimize MemoRAG's performance by enhancing its cluing mechanism and
  memorization capacity. In our experiment, MemoRAG achieves superior performance
  across a variety of evaluation tasks, including both complex ones where
  conventional RAG fails and straightforward ones where RAG is commonly applied.
paper_authors._id: 66e000965511a2f2f7325260
paper_authors.name: Hongjin Qian
paper_authors._id: 66e000965511a2f2f7325261
paper_authors.name: Peitian Zhang
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paper_authors._id: 66e000965511a2f2f7325262
paper_authors.name: Zheng Liu
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paper_authors.fullname: zhengliu
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paper_authors.name: Kelong Mao
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paper_authors.fullname: Mao
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paper_authors.name: Zhicheng Dou
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paper_authors.fullname: Zhicheng Dou
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paper_authors.user: douzc
paper_title: MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge
    Discovery
paper_summary: Retrieval-Augmented Generation (RAG) leverages retrieval tools to access
  external databases, thereby enhancing the generation quality of large language
  models (LLMs) through optimized context. However, the existing retrieval
  methods are constrained inherently, as they can only perform relevance matching
  between explicitly stated queries and well-formed knowledge, but unable to
  handle tasks involving ambiguous information needs or unstructured knowledge.
  Consequently, existing RAG systems are primarily effective for straightforward
  question-answering tasks. In this work, we propose MemoRAG, a novel
  retrieval-augmented generation paradigm empowered by long-term memory. MemoRAG
  adopts a dual-system architecture. On the one hand, it employs a light
  but long-range LLM to form the global memory of database. Once a task is
  presented, it generates draft answers, cluing the retrieval tools to locate
  useful information within the database. On the other hand, it leverages an
  expensive but expressive LLM, which generates the ultimate answer
  based on the retrieved information. Building on this general framework, we
  further optimize MemoRAG's performance by enhancing its cluing mechanism and
  memorization capacity. In our experiment, MemoRAG achieves superior performance
  across a variety of evaluation tasks, including both complex ones where
  conventional RAG fails and straightforward ones where RAG is commonly applied.
paper_ai_summary: MemoRAG enhances retrieval-augmented generation by incorporating long-term memory and a dual-system architecture, leading to superior performance across both complex and straightforward tasks.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) large language models (LLMs) long-term memory dual-system architecture global memory draft answers retrieval tools ultimate answer cluing mechanism memorization capacity