ElementCheck：基于句子元素的复杂度感知长文本事实性评估

AI 摘要

ElementCheck 提出了一种基于句子元素的复杂度感知长文本事实性评估框架，通过提取实体对构建元素图，动态调整验证粒度，以解决现有分解-检索-验证流程中的噪声和固定粒度问题。实验在 FastFact-Sent 和两个领域基准上，使用五个骨干模型验证了其一致提升事实性验证效果，并保持较好的准确率-成本平衡。

正文节选

ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements Abstract 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,

发布时间：2026-08-28 12:00

抓取时间：2026-08-28 18:10

来源机构：arXiv