用分层记忆结构生成更可靠、可追溯的维基文章
Hierarchical Memory Organization for Wikipedia Generation
- 将网页信息拆成细粒度记忆单元，递归组织成维基式层级结构
- 在新数据集上生成的文章更信息丰富且幻觉更少，引用可追踪
- 适合需要高可信度和结构化输出的自动写作场景
自主生成维基百科文章是一项挑战性任务，需整合来自多种来源的准确、全面且结构良好的信息。本文提出基于记忆组织的生成框架（MOG），通过分层记忆架构解决上述问题。MOG从网络文档中提取细粒度记忆单元，递归组织为类似维基百科的层级结构，并以此指导生成过程，确保记忆与文章大纲对齐，提升内容的信息量与可验证性，同时减少幻觉。此外，引入引用模块，将每句话关联到特定记忆单元，增强可追溯性。在新构建的WikiStart数据集上的评估表明，MOG在生成信息丰富且可靠的维基文章方面优于基线方法，尤其在真实场景中表现稳健。
原文摘要 · Abstract (English)
Generating Wikipedia articles autonomously is a challenging task requiring the integration of accurate, comprehensive, and well-structured information from diverse sources. This paper introduces the Memory Organization-based Generation (MOG) framework, a novel approach to address these challenges by leveraging a hierarchical memory architecture. MOG extracts fine-grained memory units from web documents, recursively organizes them into a Wikipedia-style hierarchical structure, and uses this structure to guide the generation process. This ensures alignment between memory and the article outline, improving both informativeness and verifiability while minimizing hallucinations. Additionally, a citation module is implemented to enhance traceability by linking every generated sentence to specific memory units. Evaluations on our newly created WikiStart dataset demonstrate that MOG outperforms baseline methods in producing informative and reliable articles, making it particularly robust in real-world scenarios.
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