STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation
To augment Large Language Models (LLMs) for multi-hop question answering, a mainstream solution within Graph Retrieval Augmented Generation (GraphRAG) leverages lightweight retrievers to efficiently extract information from a given Knowledge Graph (KG). However, existing methods often overlook the inherent challenge of sparse semantic information in graphs. Specifically, our experiments reveal that these methods produce biased retrieval Semantic Shortcut Bias and Long-Tail Path Bias, leading to inadequate semantic modeling and limited GraphRAG effectiveness. To address these issues, we propose STAR, a semantic-tuned and tail-adaptive retriever for GraphRAG. STAR integrates two key learnin...
| Authors | Shuai Li, Chen Huang, Duanyu Feng et al. |
|---|---|
| Primary category | cs.IR |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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