# ENTLORE：面向企业问答中潜在组织推理的图基基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-11 08:00
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmsy2y6810imsroz0toapfii7
- 原文链接：https://arxiv.org/abs/2608.10679

## AI 摘要

ENTLORE 是一个图基基准构建框架，从常规文档、组织表和运营记录中重建可审计的企业世界，用于测试模型从语料库中恢复隐含组织关系的能力。该基准包含 2,341 篇文档和 907 个问题，覆盖显式查找、跨源组合与潜在组织推理，已在 56 种模型与访问配置上评估。

## 正文

Enterprise question answering is framed as retrieving internal documents and generating grounded answers. Routine enterprise records, however, are work by-products in which required organizational relations remain implicit across heterogeneous sources. Existing benchmarks provide realistic multi-source evidence, but often materialize a predefined answer path and therefore test the composition of stated facts rather than recovery of a target relation absent from the corpus. We call the latter capability latent organizational reasoning. We introduce ENTLORE, a graph-grounded benchmark construction framework that reconstructs an audited enterprise world from routine documents, authoritative organizational tables, and operational records. Versioned organizational conventions certify derived relations in a truth graph, enabling complete golden answers and proof certificates. The aligned anonymized release exposes only the document corpus while withholding private structure and target relations. ENTLORE contains 2,341 documents from three source types and 907 questions spanning explicit lookup, cross-source composition, and latent organizational reasoning, evaluated across 56 model and access configurations. Structuring the released world as an induced entity graph or navigable knowledge base gives the strongest deployable results. Yet supplying gold documents still leaves 30.4% of latent questions unanswered, versus 12.6% and 6.2% for explicit and compositional questions. Enterprise QA therefore depends not only on document recall, but also on whether implicit organizational relations become usable. The benchmark, data, and code are publicly available at ENTLORE.