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Hierarchical Re-ranker Retriever (HRR)
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Hierarchical Re-ranker Retriever (HRR)
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Retrieving the right level of context for a given query is a perennial challenge in information retrieval - too large a chunk dilutes semantic specificity, while chunks that are too small lack broader context. This paper introduces the Hierarchical Re-ranker Retriever (HRR), a framework designed to achieve both fine-grained and high-level context retrieval for large language model (LLM) applications. In HRR, documents are split into sentence-level and intermediate-level (512 tokens) chunks to maximize vector-search quality for both short and broad queries. We then employ a reranker that operates on these 512-token chunks, ensuring an optimal balance neither too coarse nor too fine for robust relevance scoring. Finally, top-ranked intermediate chunks are mapped to parent chunks (2048 tokens) to provide an LLM with sufficiently large context.
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Cited by 1 Pith paper
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H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.
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