DeepWriter用离线知识库生成准确专业长文，避免幻觉和网络内容不可靠问题。
DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base
- 分步拆解任务，结合图文检索与反思式写作，确保逻辑连贯
- 在金融报告生成中事实准确率超越现有基线模型
- 适合需要高可信度、专业级输出的法律、医疗、金融领域
大型语言模型在诸多应用中表现卓越，但在金融、医疗、法律等专业领域作为写作助手时，常因缺乏深度领域知识及易产生幻觉而受限。现有方案如检索增强生成（RAG）在多步检索中存在不一致性，基于在线搜索的方法则受不可靠网页内容影响质量下降。为此，我们提出DeepWriter，一个可定制的多模态长文本写作助手，基于精心构建的离线知识库运行。其创新管道包括任务分解、提纲生成、多模态检索及分段生成与反思。通过深度挖掘结构化语料并融合文本与视觉元素，DeepWriter生成连贯、事实可靠、专业级文档。我们还提出层次化知识表示以提升检索效率与准确率。在金融报告生成任务上的实验表明，DeepWriter生成的文章在事实准确性与内容质量上均优于现有基线。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable capabilities in various applications. However, their use as writing assistants in specialized domains like finance, medicine, and law is often hampered by a lack of deep domain-specific knowledge and a tendency to hallucinate. Existing solutions, such as Retrieval-Augmented Generation (RAG), can suffer from inconsistency across multiple retrieval steps, while online search-based methods often degrade quality due to unreliable web content. To address these challenges, we introduce DeepWriter, a customizable, multimodal, long-form writing assistant that operates on a curated, offline knowledge base. DeepWriter leverages a novel pipeline that involves task decomposition, outline generation, multimodal retrieval, and section-by-section composition with reflection. By deeply mining information from a structured corpus and incorporating both textual and visual elements, DeepWriter generates coherent, factually grounded, and professional-grade documents. We also propose a hierarchical knowledge representation to enhance retrieval efficiency and accuracy. Our experiments on financial report generation demonstrate that DeepWriter produces high-quality, verifiable articles that surpasses existing baselines in factual accuracy and generated content quality.
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