Abstract
Large Language Models (LLMs) have achieved strong performance across a wide range of natural language processing tasks in recent years, including machine translation, text generation, and question answering. As their applications extend to increasingly complex scenarios, however, LLMs continue to face challenges in tasks that require deep reasoning and logical inference. In particular, models trained on large scale textual corpora may incorporate noisy or irrelevant information during generation, which can lead to incorrect predictions or outputs that are inconsistent with factual knowledge. To address this limitation, we propose a stepwise reasoning enhancement framework for LLMs based on external subgraph generation, termed SGR. The proposed framework dynamically constructs query relevant subgraphs from external knowledge bases and leverages their semantic structure to guide the reasoning process. By performing reasoning in a step by step manner over structured subgraphs, SGR reduces the influence of noisy information and improves reasoning accuracy. Specifically, the framework first generates an external subgraph tailored to the input query, then guides the model to conduct multi step reasoning grounded in the subgraph, and finally integrates multiple reasoning paths to produce the final answer. Experimental results on multiple benchmark datasets demonstrate that SGR consistently outperforms strong baselines, indicating its effectiveness in enhancing the reasoning capabilities of LLMs.
Abstract (translated)
近年来，大型语言模型（LLM）在包括机器翻译、文本生成和问答等众多自然语言处理任务中取得了优异的成绩。然而，在其应用扩展到更复杂场景时，这些模型仍然面临着需要深度推理和逻辑推断的任务的挑战。具体而言，基于大规模文本语料库训练的模型可能会在生成过程中吸收噪声或不相关的信息，导致预测错误或与事实知识不符的结果。为了应对这一限制，我们提出了一种基于外部子图生成的逐步推理增强框架SGR。该框架能够动态地从外部知识库中构建查询相关的子图，并利用其语义结构来引导推理过程。通过在结构化的子图上进行分步推理，SGR减少了噪声信息的影响并提高了推理准确性。 具体来说，该框架首先为输入的查询生成一个定制化的外部子图，然后指导模型基于此子图执行多步骤推理，最后整合多个推理路径以产生最终答案。实验结果表明，在多个基准数据集上的测试中，SGR始终优于强大的基线方法，这证明了其在增强LLM推理能力方面的有效性。
URL
https://arxiv.org/abs/2512.23356
