title: RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
summary: Retrieval-augmented language models can better adapt to changes in world
  state and incorporate long-tail knowledge. However, most existing methods
  retrieve only short contiguous chunks from a retrieval corpus, limiting
  holistic understanding of the overall document context. We introduce the novel
  approach of recursively embedding, clustering, and summarizing chunks of text,
  constructing a tree with differing levels of summarization from the bottom up.
  At inference time, our RAPTOR model retrieves from this tree, integrating
  information across lengthy documents at different levels of abstraction.
  Controlled experiments show that retrieval with recursive summaries offers
  significant improvements over traditional retrieval-augmented LMs on several
  tasks. On question-answering tasks that involve complex, multi-step reasoning,
  we show state-of-the-art results; for example, by coupling RAPTOR retrieval
  with the use of GPT-4, we can improve the best performance on the QuALITY
  benchmark by 20% in absolute accuracy.
paper_authors._id: 65bb083bf8b6408e7f2feab4
paper_authors.name: Parth Sarthi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-02T09:01:30.143Z
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paper_authors.fullname: Parth Sarthi
paper_authors.type: user
paper_authors.user: psarthi
paper_authors._id: 65bb083bf8b6408e7f2feab5
paper_authors.name: Salman Abdullah
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-08T09:50:20.618Z
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paper_authors.fullname: Salman Abdullah
paper_authors.type: user
paper_authors.user: salman-abdullah
paper_authors._id: 65bb083bf8b6408e7f2feab6
paper_authors.name: Aditi Tuli
paper_authors._id: 65bb083bf8b6408e7f2feab7
paper_authors.name: Shubh Khanna
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-01T11:29:22.728Z
paper_authors._id: 639481afacf644bed584e8de
paper_authors.avatarUrl: /avatars/67be7c6058f3fa5106b8691b2554146b.svg
paper_authors.fullname: Shubh Khanna
paper_authors.type: user
paper_authors.user: skhanna
paper_authors._id: 65bb083bf8b6408e7f2feab8
paper_authors.name: Anna Goldie
paper_authors._id: 65bb083bf8b6408e7f2feab9
paper_authors.name: Christopher D. Manning
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-01T11:30:04.209Z
paper_authors._id: 5f555a1f8bf55658acfed19e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1628695407761-5f555a1f8bf55658acfed19e.jpeg
paper_authors.fullname: Christopher Manning
paper_authors.type: user
paper_authors.user: manning
paper_title: RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
paper_summary: Retrieval-augmented language models can better adapt to changes in world
  state and incorporate long-tail knowledge. However, most existing methods
  retrieve only short contiguous chunks from a retrieval corpus, limiting
  holistic understanding of the overall document context. We introduce the novel
  approach of recursively embedding, clustering, and summarizing chunks of text,
  constructing a tree with differing levels of summarization from the bottom up.
  At inference time, our RAPTOR model retrieves from this tree, integrating
  information across lengthy documents at different levels of abstraction.
  Controlled experiments show that retrieval with recursive summaries offers
  significant improvements over traditional retrieval-augmented LMs on several
  tasks. On question-answering tasks that involve complex, multi-step reasoning,
  we show state-of-the-art results; for example, by coupling RAPTOR retrieval
  with the use of GPT-4, we can improve the best performance on the QuALITY
  benchmark by 20% in absolute accuracy.
paper_ai_summary: The RAPTOR model enhances retrieval-augmented language models by recursively embedding, clustering, and summarizing text chunks, leading to better performance on question-answering tasks involving complex reasoning.
paper_ai_keywords: retrieval-augmented language models Recursive embedding clustering summarizing tree structure abstraction levels RAPTOR model question-answering tasks multi-step reasoning QuALITY benchmark