HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912639182962688 |
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| author | Lu, Wensheng Chen, Keyu Qiao, Ruizhi Sun, Xing |
| author_facet | Lu, Wensheng Chen, Keyu Qiao, Ruizhi Sun, Xing |
| contents | Retrieval-Augmented Generation (RAG) enhances the response capabilities of language models by integrating external knowledge sources. However, document chunking as an important part of RAG system often lacks effective evaluation tools. This paper first analyzes why existing RAG evaluation benchmarks are inadequate for assessing document chunking quality, specifically due to evidence sparsity. Based on this conclusion, we propose HiCBench, which includes manually annotated multi-level document chunking points, synthesized evidence-dense quetion answer(QA) pairs, and their corresponding evidence sources. Additionally, we introduce the HiChunk framework, a multi-level document structuring framework based on fine-tuned LLMs, combined with the Auto-Merge retrieval algorithm to improve retrieval quality. Experiments demonstrate that HiCBench effectively evaluates the impact of different chunking methods across the entire RAG pipeline. Moreover, HiChunk achieves better chunking quality within reasonable time consumption, thereby enhancing the overall performance of RAG systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking Lu, Wensheng Chen, Keyu Qiao, Ruizhi Sun, Xing Computation and Language Artificial Intelligence Retrieval-Augmented Generation (RAG) enhances the response capabilities of language models by integrating external knowledge sources. However, document chunking as an important part of RAG system often lacks effective evaluation tools. This paper first analyzes why existing RAG evaluation benchmarks are inadequate for assessing document chunking quality, specifically due to evidence sparsity. Based on this conclusion, we propose HiCBench, which includes manually annotated multi-level document chunking points, synthesized evidence-dense quetion answer(QA) pairs, and their corresponding evidence sources. Additionally, we introduce the HiChunk framework, a multi-level document structuring framework based on fine-tuned LLMs, combined with the Auto-Merge retrieval algorithm to improve retrieval quality. Experiments demonstrate that HiCBench effectively evaluates the impact of different chunking methods across the entire RAG pipeline. Moreover, HiChunk achieves better chunking quality within reasonable time consumption, thereby enhancing the overall performance of RAG systems. |
| title | HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.11552 |