CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents
Retrieval-Augmented Generation (RAG) systems lose retrieval accuracy when similar documents coexist in the vector database, causing unnecessary information, hallucinations, and factual errors. To alleviate this issue, we propose CHOP, a framework that iteratively evaluates chunk relevance with Large Language Models (LLMs) and progressively reconstructs documents by determining their association with specific topics or query types. CHOP integrates two key components: the CNM-Extractor, which generates compact per-chunk signatures capturing categories, key nouns, and model names, and the Continuity Decision Module, which preserves contextual coherence by deciding whether consecutive chunks ...
| Authors | Hyunseok Park, Jihyeon Kim, Jongeun Kim et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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