Title: Mixture-of-Experts for Knowledge Graph Retrieval-Augmented Generation
Primary area: foundation or frontier models, including LLMs
Keywords: Knowledge Graph, Question Answering, Large Language Model, RAG, Mixture-of-Experts

Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in open-domain question answering, but often struggle with factual accuracy and multi-hop reasoning due to the incompleteness of the training corpora. A promising solution is Knowledge Graph Retrieval-Augmented Generation (KG-RAG), which supplements LLMs with structured knowledge retrieved from external knowledge graphs (KGs). However, existing KG-RAG methods either rely on large-scale language models (e.g., ChatGPT) to guide the retrieval process, which leads to high computational costs, or suffer from limited retrieval quality when using lightweight language models, particularly under multi-hop scenarios. We propose MoRA (Mixture-of-Experts for Retrieval-Augmented Generation over Knowledge Graphs), a novel KG-RAG framework that enhances hop-wise KG knowledge retrieval through a Mixture-of-Experts (MoE) framework. Each expert is guided by a combination of two types of soft prompts: expert-specific soft prompt encourages specialization in different reasoning perspectives across experts, and contextual soft prompt evolves with each reasoning hop by encoding the query and previously explored KG triplets, enabling the model to preserve consistency and relevance across multi-hop retrieval. This design allows MoRA to perform accurate and robust retrieval using lightweight language models. MoRA achieves superior performance across multiple KG-based Question Answering benchmarks compared to existing retrieval systems, including those that rely on much larger language models, demonstrating its effectiveness under limited computational budgets.