发表机构
HKUST(GZ)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出EidosDoc系统，通过隐式结构编码、混合检索和动态证据扩展，在四个基准上实现最先进准确率，同时成本降低50倍、延迟降低4倍。
AI 中文摘要
半结构化文档在科学报告、财务报表和技术手册中无处不在。对此类文档进行问答需要同时理解文本、表格、图表和复杂的层级布局。现有方法要么依赖反复调用大型语言模型进行结构解析和检索，导致高成本和较大延迟，要么将文档扁平化而丢失布局和层级信息，从而牺牲答案准确性。为解决这一问题，我们提出了EidosDoc，一种以最小计算开销实现最先进准确率的新型系统。我们的方法引入了三项核心创新。（1）隐式结构编码器，通过对比学习和结构一致性损失进行训练。该模块将层级关系、空间位置和文本内容联合嵌入到稠密向量空间中，整体性地捕获文档结构，无需手动定义且易出错的结构。（2）混合检索流水线，利用BM25、布局指纹和轻量级交叉编码器，完全在不调用LLM的情况下执行高精度检索，大幅降低成本和延迟。（3）动态证据扩展机制，自适应地检索空间相邻和结构相关的证据，克服固定路径检索方法中常见的证据遗漏问题。我们在四个基准上评估了EidosDoc，综合评估表明EidosDoc在四个基准上取得了新的最先进准确率。关键在于，与之前最先进的方法相比，其成本降低了50倍，延迟降低了4倍。这些结果表明，EidosDoc在准确性、成本和速度之间建立了新的最优权衡，为准确的半结构化文档分析提供了一条实用且可扩展的路径。
英文摘要
Semi-structured documents are ubiquitous in scientific reports, financial statements, and technical manuals. Question answering over such documents requires simultaneous understanding of text, tables, charts, and complex hierarchical layouts. Existing methods either rely on repeatedly calling large language models for structure parsing and retrieval, leading to high cost and large latency, or they flatten the document and lose layout and hierarchy information, sacrificing answer accuracy. To address this, we propose EidosDoc, a novel system that achieves state-of-the-art accuracy with minimal computational expense. Our approach introduces three core innovations. (1) An Implicit Structure Encoder trained via contrastive learning and a structure consistency loss. This module jointly embeds hierarchical relationships, spatial positions, and textual content into a dense vector space, capturing document structure holistically without the need for manually defined and error-prone constructions. (2) A Hybrid Retrieval Pipeline that leverages BM25, layout fingerprints, and a lightweight cross-encoder to perform high-precision retrieval entirely without invoking an LLM, drastically reducing cost and latency. (3) A Dynamic Evidence Expansion mechanism that adaptively retrieves spatially adjacent and structurally related evidence, overcoming the evidence omission common in fixed-path retrieval methods. We evaluate EidosDoc on four benchmarks, and comprehensive evaluations show that EidosDoc achieves a new state-of-the-art accuracy on the four benchmarks. Crucially, it does so with a 50 times reduction in cost and 4 times lower latency compared to the previous state-of-the-art Method. These results demonstrate that EidosDoc establishes a new optimal trade-off among accuracy, cost, and speed, offering a practical and scalable path for accurate semi-structured document analysis.
