The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
This work addresses accessibility and scalability issues in knowledge graph integration for small language models, though it is incremental as it builds on existing methods.
The study tackled the problem of limited knowledge graph traversal and reasoning in small language models for question answering by proposing lightweight exploration modules, resulting in improved performance on KG-based tasks.
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/.