SeaKR：自我意識知識檢索，用於適應性檢索增強生成

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

本文介紹了自我意識知識檢索（SeaKR），這是一種新型的適應性RAG模型，從LLM的內部狀態中提取自我意識不確定性。SeaKR在LLM呈現高自我意識不確定性以進行生成時啟動檢索。為了有效整合檢索到的知識片段，SeaKR根據LLM的自我意識不確定性對它們進行重新排序，以保留最大程度降低其不確定性的片段。為了促進解決需要多次檢索的複雜任務，SeaKR利用它們的自我意識不確定性來選擇不同的推理策略。我們在複雜和簡單的問答數據集上進行的實驗表明，SeaKR優於現有的適應性RAG方法。我們在https://github.com/THU-KEG/SeaKR 上發布了我們的代碼。

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.