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.

翻译：本文提出自感知知识检索（SeaKR），这是一种新颖的自适应RAG模型，能够从大语言模型（LLM）的内部状态中提取其自感知不确定性。当LLM在生成过程中表现出高自感知不确定性时，SeaKR会激活检索机制。为有效整合检索到的知识片段，SeaKR基于LLM的自感知不确定性对片段进行重排序，以最大限度地保留那些能降低其不确定性的片段。为解决需要多次检索的复杂任务，SeaKR利用自感知不确定性在不同推理策略中进行选择。我们在复杂和简单问答数据集上的实验表明，SeaKR优于现有的自适应RAG方法。代码已发布于https://github.com/THU-KEG/SeaKR。