Hierarchical context enhancement for long-tail entity retrieval augmented generation
Yixuan Peng, Kewu Pan · Frontiers in Artificial Intelligence · 2026
Introduction: Retrieval-Augmented Generation (RAG) in Domain-specific Question Answering (DSQA) often faces significant performance degradation due to semantic drift. Our analysis reveals that the main cause is the absence of a dedicated mechanism for handling low-frequency terms. Methods: Motivated by this observation, we propose a hierarchical context enhancement retrieval augmented generation (HCE-RAG). Specifically, in the indexing stage, we anchor low-frequency entities offline through entity-sensitive contextual tagging. During query processing, we perform minimal yet entity-focused query clarification via constrained query reflection. Finally, in the retrieval stage, we employ hybrid retrieval with RRF to balance contextual signals and exact word matching, thereby enabling robust identification of low-frequency entities. Results: Experiments on a dedicated domain specific QA benchmark show that our method achieves strong results, delivering a 29 percentage point gain in Recall@10 on low-frequency entities. Discussion: Notably, the proposed approach is plug-and-play and can be directly integrated with existing state-of-the-art algorithms to improve their response accuracy in long-tail entity question answering.