SeaKR：自我意识知识检索，用于增强检索辅助生成

SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation

June 27, 2024

作者: Zijun Yao, Weijian Qi, Liangming Pan, Shulin Cao, Linmei Hu, Weichuan Liu, Lei Hou, Juanzi Li

cs.AI

摘要

本文介绍了自我意识知识检索（SeaKR），这是一种新颖的自适应RAG模型，从LLM的内部状态中提取自我意识的不确定性。SeaKR在LLM呈现高自我意识不确定性以进行生成时激活检索。为了有效整合检索到的知识片段，SeaKR根据LLM的自我意识不确定性重新对它们进行排名，以保留能最大程度降低其不确定性的片段。为了便于解决需要多次检索的复杂任务，SeaKR利用它们的自我意识不确定性来选择不同的推理策略。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。我们在https://github.com/THU-KEG/SeaKR 上发布了我们的代码。

English

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.