The interest in updating Large Language Models (LLMs) without retraining from
scratch is substantial, yet it comes with some challenges.This is especially
true for situations demanding complex reasoning with limited samples, a
scenario we refer to as the Paucity-Constrained Complex Reasoning Adaptation
for LLMs (PCRA-LLM).Traditional methods like Low-Rank Adaptation (LoRA) and
Retrieval-Augmented Generation (RAG) are inadequate for this critical issue,
particularly evident in our exploration of a specific medical context that
epitomize the PCRA-LLM's distinct needs.To address the issue, we propose a
Sequential Fusion method to incorporate knowledge from complex context into
LLMs. This method employs a two-stage framework: initially, it leverages
general LLMs to construct knowledge graphs (KGs) for extracting knowledge from
complex texts; subsequently, it updates the domain LLMs through knowledge edit.
According to our method, the domain LLM achieved a 71.69\% accuracy in question
answering tasks. Subsequently, we broadened our assessment to a novel dataset
we developed in the economics and management field, where our method realized a
75\% accuracy. These outcomes underline the efficacy and adaptability of our
approach for PCRA-LLM across various domains.

基于复杂情境的知识融合方法推进领域语言模型在问题回答任务上的准确率，展示了适应 Paucity-Constrained 复杂推理适应于各个领域的高效性和适应性。