用轻量探索模块提升小模型答知识图谱题能力
The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
- 用简单模块替代小模型进行知识图谱遍历
- 小模型在知识图谱问答任务上性能显著提升
- 适合资源有限但需精准推理的场景
将知识图谱（KG）融入大型语言模型（LLMs）的推理过程，是缓解幻觉现象的有前景方法。然而，现有研究多依赖专有或极大型模型，限制了可及性与可扩展性。本研究探讨了现有集成方法在小型语言模型（SLMs）上用于知识图谱问答的能力，发现其性能常受限于图谱遍历与推理能力不足。为此，我们提出利用简单高效的探索模块，代替语言模型自身完成知识图谱遍历。实验表明，这些轻量级模块能有效提升小模型在知识图谱问答任务上的表现。源代码：https://github.com/yijie-cheng/SLM-ToG/
原文摘要 · Abstract (English)
Integrating knowledge graphs (KGs) into the reasoning processes of large language models (LLMs) has emerged as a promising approach to mitigate hallucination. However, existing work in this area often relies on proprietary or extremely large models, limiting accessibility and scalability. In this study, we investigate the capabilities of existing integration methods for small language models (SLMs) in KG-based question answering and observe that their performance is often constrained by their limited ability to traverse and reason over knowledge graphs. To address this limitation, we propose leveraging simple and efficient exploration modules to handle knowledge graph traversal in place of the language model itself. Experiment results demonstrate that these lightweight modules effectively improve the performance of small language models on knowledge graph question answering tasks. Source code: https://github.com/yijie-cheng/SLM-ToG/.
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