SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented
  Generation
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.
667e1f61e677941031fd7de6
Zijun Yao
claimed_verified
2024-06-28T08:02:55.154Z
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Zijun
user
TranSirius
667e1f61e677941031fd7de7
Weijian Qi
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Liangming Pan
admin_assigned
2024-06-28T11:43:43.362Z
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Liangming Pan
user
Liangming
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Shulin Cao
admin_assigned
2024-06-28T11:43:36.918Z
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ShulinCao
user
caoshulin
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Linmei Hu
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Weichuan Liu
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Lei Hou
667e1f61e677941031fd7ded
Juanzi Li
admin_assigned
2024-06-28T11:43:22.144Z
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/avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
Juanzi Li
user
juanli
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented
  Generation
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.
SeaKR, an adaptive RAG model, improves knowledge retrieval by leveraging LLMs' self-aware uncertainty to activate, re-rank, and integrate knowledge appropriately, outperforming existing methods in QA tasks.
Self-aware Knowledge Retrieval SeaKR adaptive RAG self-aware uncertainty LLMs internal states knowledge snippets re-ranking reasoning strategies QA datasets