title: SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented
    Generation
summary: This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel
  adaptive RAG model that extracts self-aware uncertainty of LLMs from their
  internal states. SeaKR activates retrieval when the LLMs present high
  self-aware uncertainty for generation. To effectively integrate retrieved
  knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty
  to preserve the snippet that reduces their uncertainty to the utmost. To
  facilitate solving complex tasks that require multiple retrievals, SeaKR
  utilizes their self-aware uncertainty to choose among different reasoning
  strategies. Our experiments on both complex and simple Question Answering
  datasets show that SeaKR outperforms existing adaptive RAG methods. We release
  our code at https://github.com/THU-KEG/SeaKR.
paper_authors._id: 667e1f61e677941031fd7de6
paper_authors.name: Zijun Yao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-28T08:02:55.154Z
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paper_authors.fullname: Zijun
paper_authors.type: user
paper_authors.user: TranSirius
paper_authors._id: 667e1f61e677941031fd7de7
paper_authors.name: Weijian Qi
paper_authors._id: 667e1f61e677941031fd7de8
paper_authors.name: Liangming Pan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T11:43:43.362Z
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paper_authors.avatarUrl: /avatars/cf64c88e71c730fc0a54df59e17bffbb.svg
paper_authors.fullname: Liangming Pan
paper_authors.type: user
paper_authors.user: Liangming
paper_authors._id: 667e1f61e677941031fd7de9
paper_authors.name: Shulin Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T11:43:36.918Z
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paper_authors.fullname: ShulinCao
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paper_authors.user: caoshulin
paper_authors._id: 667e1f61e677941031fd7dea
paper_authors.name: Linmei Hu
paper_authors._id: 667e1f61e677941031fd7deb
paper_authors.name: Weichuan Liu
paper_authors._id: 667e1f61e677941031fd7dec
paper_authors.name: Lei Hou
paper_authors._id: 667e1f61e677941031fd7ded
paper_authors.name: Juanzi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T11:43:22.144Z
paper_authors._id: 65df8cbc2705d9672f55d1aa
paper_authors.avatarUrl: /avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
paper_authors.fullname: Juanzi Li
paper_authors.type: user
paper_authors.user: juanli
paper_title: SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented
    Generation
paper_summary: This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel
  adaptive RAG model that extracts self-aware uncertainty of LLMs from their
  internal states. SeaKR activates retrieval when the LLMs present high
  self-aware uncertainty for generation. To effectively integrate retrieved
  knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty
  to preserve the snippet that reduces their uncertainty to the utmost. To
  facilitate solving complex tasks that require multiple retrievals, SeaKR
  utilizes their self-aware uncertainty to choose among different reasoning
  strategies. Our experiments on both complex and simple Question Answering
  datasets show that SeaKR outperforms existing adaptive RAG methods. We release
  our code at https://github.com/THU-KEG/SeaKR.
paper_ai_keywords: Self-aware Knowledge Retrieval (SeaKR) adaptive RAG model self-aware uncertainty LLMs (Large Language Models) knowledge snippets re-ranking reasoning strategies complex tasks Question Answering datasets