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 this https URL

介绍了一种自我感知知识检索模型（SeaKR），该模型从大规模语言模型（LLMs）的内部状态中提取自我感知的不确定性，通过检测 LLMs 生成时的高度不确定性来激活检索。为了有效地整合已检索的知识片段，SeaKR 根据 LLMs 的自我感知不确定性对其进行重新排序，以保留最大程度地减少不确定性的片段。为了解决需要多次检索的复杂任务，SeaKR 利用自我感知不确定性来选择不同的推理策略。在复杂和简单问答数据集上的实验证明，SeaKR 优于现有的自适应 RAG 方法。我们将代码发布在此 https URL。