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 , 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}, 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, 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.

通过对 LLMs 的选择性整合外部知识，介绍了一种基于多个 LLM 之间交互协助的外部推理新方法 (ChatPDF)，在人类反馈的响应下，根据查询的复杂性调整支持水平，经过全面评估后取得了最先进的性能，并且相比 LLMs 直接处理全文，这种方法更加高效。