Abstract: Large Language Models exhibit powerful in-context learning and text generation capabilities, showing significant potential in tasks such as information retrieval and presentation writing. However, their ability is often insufficient when dealing with tasks that demand high timeliness, truthfulness, and specificity/format requirements, such as generating formatted documents in specific domains, where effective methods are still lacking. Consequently, it is necessary to integrate both agent technology and model fine-tuning techniques. This paper proposes a formatted document generation method that combines an LLM-based Agent architecture with Large Language Model fine-tuning. The LLM-based Agent architecture is utilized to acquire and verify real-time news information, which in turn is used to construct a domain-specific LLM fine-tuning dataset. Subsequently, fine-tuning techniques are employed to enhance the model's ability to generate style-compliant (normative) text. The method was tested, optimized, and validated using datasets from different domains. Experimental results demonstrate that the proposed method outperforms baseline approaches across evaluation metrics such as semantic similarity and text similarity. This indicates that the proposed method effectively strengthens the model's understanding and text generation capabilities for specific domains, and provides reliable guarantees for the timeliness and truthfulness of the generated text.

摘要： 大语言模型具有强大的上下文学习和文本生成能力，在信息检索与简报写作等任务中潜力显著，但在处理对于时效性、真实性以及规范性有较高要求的任务时能力不足，例如在特定领域的格式化文档生成方面仍缺少有效方法。因此需要将智能体技术和模型微调技术两者结合。该文提出了融合大模型智能体架构与大语言模型微调的格式化文档生成方法，通过大模型智能体架构实现实时新闻信息的获取并验证过滤，构建特定领域大模型微调数据集，采用微调技术增强其生成风格规范文本的能力。在不同领域数据集下进行了测试优化与效果验证，实验结果表明该方法在语义相似度、文本相似性等评价指标上性能均优于基线方法。表明该方法可有效强化模型对特定领域的理解与文本生成能力，并为生成文本的时效性与真实性提供可靠保障。