发表机构
Institute of Automation, Chinese Academy of Sciences; Zhongguancun Academy; Fudan University; Tencent; University of Cambridge; University of Aberdeen（中国科学院自动化研究所; 中关村学院; 复旦大学; 腾讯; 剑桥大学; 阿伯丁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ElementCheck是一种基于句子元素的复杂度感知框架，通过提取实体对构建元素图，在FastFact-Sent等基准上提升了五个主干模型的长文本事实性验证性能，且优化了准确率与成本的权衡。
AI 中文摘要
现有的长文本事实性评估依赖分解-检索-验证流程，但该流程存在主张分解产生的噪声以及固定验证粒度的问题，导致结果不可靠。我们提出ElementCheck，这是一种基于句子元素验证长文本输出的复杂度感知框架。ElementCheck并非将句子统一分解为原子子主张，而是提取原句中通过可验证连接显式关联的实体对作为元素，并将其组织为元素图。图拓扑结构提供了用于估计句子复杂度的结构信号，支持对简单句子直接验证，对复杂句子则进行针对性的元素级细化与验证。为支持细粒度评估，我们将FastFact-Bench中的孤立主张映射回其源句子，构建了新基准FastFact-Sent。在FastFact-Sent及两个领域特定基准上的实验表明，ElementCheck在五个主干模型上均提升了事实性验证性能，同时保持了良好的准确率-成本权衡。进一步分析显示，复杂度感知验证减少了不必要的重新验证，并在不同主干模型间保持了稳定性。
英文摘要
Existing long-form factuality evaluation relies on the decompose-retrieve-verify pipeline. However, the pipeline suffers from noise from claim decomposition and fixed verification granularity, resulting in unreliable results. We propose ElementCheck, a complexity-aware framework that verifies long-form outputs via sentence elements. Instead of uniformly decomposing sentences into atomic sub-claims, ElementCheck extracts entity pairs that are explicitly linked through verifiable connections in the original sentence as elements, and organizes these into an element graph. The graph topology provides a structural signal for estimating sentence complexity, enabling direct verification for simple sentences and targeted element-level refinement and verification for complex ones. To support fine-grained evaluation, we construct a new benchmark \textbf{FastFact-Sent} by mapping isolated claims from FastFact-Bench back to their source sentences. Experiments on FastFact-Sent and two domain-specific benchmarks show ElementCheck consistently improves factuality verification across five backbone models while maintaining a favorable accuracy-cost trade-off. Further analyses demonstrate that complexity-aware verification reduces unnecessary re-verification and maintains stability across different backbones. The code is available at \href{https://github.com/gudehhh666/elementcheck.git}{Here}.
CommentsEMNLP2026 Findings
