结构与多样性感知的上下文气泡构建方法用于企业检索增强系统
Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems
AI总结：
本文提出一种结构感知和多样性约束的上下文气泡构建方法，以提高企业检索增强系统中上下文的效率和信息完整性。
摘要：
大型语言模型（LLM）的上下文通常通过检索增强生成（RAG）来构建，该方法涉及对文档的排序和选择前k个段落。这种方法导致了文档结构中的信息图碎片化、过度检索、内容重复以及查询上下文不足，包括二阶和三阶特征。本文提出了一种结构感知和多样性约束的上下文气泡构建框架，该框架在严格的token预算下组装出连贯且可引用的span集合。该方法通过组织多粒度的span（例如章节和行）并利用任务条件的结构先验来指导检索。从高相关性锚定span开始，通过受约束的选择构建上下文气泡，平衡查询相关性、边际覆盖和冗余惩罚。它会显式约束多样性和预算，产生紧凑且信息丰富的上下文集合，不同于top-k检索。此外，一个完整的检索被发出，追踪记录的评分和选择选择，从而提供可审计性和确定性调节。在企业文档上的实验表明，上下文气泡的效率体现在显著减少冗余上下文、更好地覆盖二次特征以及在有限的上下文窗口内具有更好的回答质量和引用可信度。消融研究显示，结构先验和多样性约束选择都是必要的；移除任一组件会导致覆盖减少和冗余或不完整的上下文增加。
英文摘要：
Large language model (LLM) contexts are typically constructed using retrieval-augmented generation (RAG), which involves ranking and selecting the top-k passages. The approach causes fragmentation in information graphs in document structures, over-retrieval, and duplication of content alongside insufficient query context, including 2nd and 3rd order facets. In this paper, a structure-informed and diversity-constrained context bubble construction framework is proposed that assembles coherent, citable bundles of spans under a strict token budget. The method preserves and exploits inherent document structure by organising multi-granular spans (e.g., sections and rows) and using task-conditioned structural priors to guide retrieval. Starting from high-relevance anchor spans, a context bubble is constructed through constrained selection that balances query relevance, marginal coverage, and redundancy penalties. It will explicitly constrain diversity and budget, producing compact and informative context sets, unlike top-k retrieval. Moreover, a full retrieval is emitted that traces the scoring and selection choices of the records, thus providing auditability and deterministic tuning. Experiments on enterprise documents demonstrate the efficiency of context bubble as it significantly reduces redundant context, is better able to cover secondary facets and has a better answer quality and citation faithfulness within a limited context window. Ablation studies demonstrate that both structural priors as well as diversity constraint selection are necessary; removing either component results in a decline in coverage and an increase in redundant or incomplete context.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
