The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Yi-Jie Cheng, Oscar Chew, Yun-Nung Chen
Institution: National Taiwan University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889395