Knowledge Pull Requests:面向持续文档写作的知识变更框架

HuggingFace Daily Papers(社区热门论文)·2026-09-22 08:00·2天前
AI 导读

研究者提出 Knowledge Pull Requests(KPRs)框架,通过提取新知识中的论断、过滤并路由到对应章节、标记与现有内容的冲突,生成区分知识变更(claim proposal)与文本变更(document diff)的 ChangeLog。

HuggingFace Daily Papers(社区热门论文)
40AI 编辑部评分,满分 100

Knowledge Pull Requests:面向持续文档写作的知识变更框架

2026-09-22 08:00· 2天前
AI 导读

研究者提出 Knowledge Pull Requests(KPRs)框架,通过提取新知识中的论断、过滤并路由到对应章节、标记与现有内容的冲突,生成区分知识变更(claim proposal)与文本变更(document diff)的 ChangeLog。

We introduce Knowledge Pull Requests (KPRs), a framework for continual document authoring that makes each change interpretable. Documents require ongoing revision as new knowledge surfaces from other sources, languages, or times, but existing approaches either edit with no account of what knowledge changed or regenerate from scratch. A KPR integrates new knowledge into a document by extracting claims, filtering and routing them to sections, and flagging conflicts with existing content, producing a ChangeLog that separates what knowledge changes (claim proposal) from how the text changes (document diff). We evaluate KPRs on revising Wikipedia across languages and updating query-driven reports on RAGTIME. KPRs integrate more information and better preserve existing content than rewriting from sources or regenerating from scratch, while adding the most information per token generated. A KPR-revised article also grounds question answering better than a frontier model with search, which does not surface knowledge documented only in other languages.

来源:HuggingFace Daily Papers(社区热门论文)· arxiv.org