SbSER: Step-by-Step Enhanced Reasoning Framework for Large Language Model with External Subgraph Generation - FENG Tuoyu, WANG Gangliang, QIAO Zijian, LI Weiping, ZHANG Yusong, GUO Qinglang (Jisuanji kexue yu tansuo)
Large language models (LLMs) have achieved significant success across various tasks, particularly in machine translation, text generation, and question-answering systems since their inception. Their applications have rapidly expanded to more complex tasks. However, despite their impressive performance in many areas, LLMs still face significant challenges in tasks that require deep reasoning and logical deduction. This is mainly due to the fact that during training, LLMs rely heavily on large volumes of textual data, which often fail to comprehensively cover specialized knowledge across all domains. As a result, LLMs tend to generate “hallucinations” in handling domain-specific problems, meaning they output inaccurate or factually incorrect answers. This issue can be mitigated by incorporating external knowledge graphs (KG) to assist in the reasoning process of LLMs. This paper presents the SbSER, a step-by-step enhanced reasoning framework for LLMs with external subgraph generation. Firstly, it guides the LLMs to do accurate semantic parsing through generating clear subgraph Schemas, converting questions into logical retrieval statements. Secondly, it imports knowledge triples into a graph database to complete precise knowledge retrieval. Finally, it achieves the final enhanced reasoning answer through the combination of two reasoning methods: direct retrieval reasoning and joint retrieval reasoning. Experiments demonstrate that the proposed SbSER achieves outstanding results across multiple datasets, significantly enhancing the ability of LLMs to solve complex problems.