Abstract
Retrieval-Augmented Generation (RAG) systems critically depend on effective document chunking strategies to balance retrieval quality, latency, and operational cost. Traditional chunking approaches, such as fixed-size, rule-based, or fully agentic chunking, often suffer from high token consumption, redundant text generation, limited scalability, and poor debuggability, especially for large-scale web content ingestion. In this paper, we propose Web Retrieval-Aware Chunking (W-RAC), a novel, cost-efficient chunking framework designed specifically for web-based documents. W-RAC decouples text extraction from semantic chunk planning by representing parsed web content as structured, ID-addressable units and leveraging large language models (LLMs) only for retrieval-aware grouping decisions rather than text generation. This significantly reduces token usage, eliminates hallucination risks, and improves system this http URL analysis and architectural comparison demonstrate that W-RAC achieves comparable or better retrieval performance than traditional chunking approaches while reducing chunking-related LLM costs by an order of magnitude.
Abstract (translated)
检索增强生成（RAG）系统严重依赖于有效的文档分块策略，以平衡检索质量、延迟和运营成本。传统的分块方法（如固定大小、基于规则或完全智能体的分块）通常存在令牌消耗高、文本生成冗余、可扩展性有限以及可调试性差等问题，尤其在大规模网络内容摄入场景下更为突出。本文提出了一种面向网络文档的新型低成本分块框架——网络检索感知分块（W-RAC）。W-RAC通过将解析后的网络内容表示为结构化、ID可寻址的单元，并仅利用大语言模型（LLM）进行检索感知的分组决策（而非文本生成），实现了文本提取与语义分块规划的分离。这显著降低了令牌使用量，消除了幻觉风险，并提升了系统可扩展性和可调试性。系统分析与架构对比表明，W-RAC在实现与传统分块方法相当或更优的检索性能的同时，将分块相关的LLM成本降低了一个数量级。
URL
https://arxiv.org/abs/2604.04936
