Memory is identified as a crucial human faculty that allows for the retention of visual and linguistic information within the hippocampus and neurons in the brain, which can subsequently be retrieved to address real-world challenges that arise through a lifetime of learning. The resolution of complex AI tasks through the application of acquired knowledge represents a stride toward the realization of artificial general intelligence. However, despite the prevalence of Large Language Models (LLMs) like GPT-3.5 and GPT-4 \cite{brown2020language, leiter2023chatgpt, zaitsu2023distinguishing, OpenAI2023GPT4TR} , which have displayed remarkable capabilities in language comprehension, generation, interaction, and reasoning, they are inhibited by constraints on context length that preclude the processing of extensive, continually evolving knowledge bases. This paper proposes that LLMs could be augmented through the selective integration of knowledge from external repositories, and in doing so, introduces a novel methodology for External Reasoning, exemplified by ChatPDF. Central to this approach is the establishment of a tiered policy for \textbf{External Reasoning based on Multiple LLM Interchange Assistance} in \cref{fig:overall}, where the level of support rendered is modulated across entry, intermediate, and advanced tiers based on the complexity of the query, with adjustments made in response to human feedback. A comprehensive evaluation of this methodology is conducted using multiple LLMs and the results indicate state-of-the-art performance in \cref{comparison} , surpassing existing solutions including ChatPDF.com. Moreover, the paper emphasizes that this approach is more efficient compared to the direct processing of full text by LLMs. The source code is publicly available at: \url{https://github.com/AkideLiu/ANLP}.

翻译：记忆被视为人类至关重要的能力，它使得大脑海马体和神经元能够保留视觉与语言信息，并在后续的学习过程中提取这些信息以应对现实世界的挑战。通过应用所学知识解决复杂的人工智能任务，是实现通用人工智能的重要一步。然而，尽管GPT-3.5、GPT-4等大语言模型在语言理解、生成、交互和推理方面展现出卓越能力，但它们受限于上下文长度约束，无法处理不断演变的大规模知识库。本文提出，通过选择性整合外部知识库中的知识，可增强大语言模型的能力，并以此为基础引入了一种新的外部推理方法，以ChatPDF为例进行说明。该方法的核心在于建立基于多模型可互换辅助的层级策略（外部推理，见图\cref{fig:overall}），根据查询的复杂度划分为入门、中级和高级三个层级，并根据人类反馈动态调整支持程度。通过多种大语言模型对该方法进行综合评估，结果表明\cref{comparison}中该方法达到了最先进的性能，超越了包括ChatPDF.com在内的现有解决方案。此外，本文强调该方法相比大语言模型直接处理全文更为高效。源代码已公开于：\url{https://github.com/AkideLiu/ANLP}。