用多智能体和检索增强生成，自动写新闻并模拟公众反馈。
AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models
- 多智能体协作分工，提升新闻专业性
- 模拟不同人群反馈，提前预判舆论反应
- 适合需要快速响应的新闻机构与舆情研究者
社交媒体平台的兴起重塑了新闻业。内容需求激增促使大型语言模型（LLMs）被广泛应用于新闻生产，因其速度快、成本低。然而，LLMs在专业性和伦理判断上仍存局限，且发布前难以预测公众反馈。为此，我们提出AI-Press，一个基于多智能体协同与检索增强生成（RAG）的自动化新闻撰写与润色系统。我们还构建了一个考虑人口分布特征的公众反馈模拟模块。通过大量定量与定性评估，系统在新闻生成能力上表现显著提升，并验证了反馈模拟的有效性。
原文摘要 · Abstract (English)
The rise of various social platforms has transformed journalism. The growing demand for news content has led to the increased use of large language models (LLMs) in news production due to their speed and cost-effectiveness. However, LLMs still encounter limitations in professionalism and ethical judgment in news generation. Additionally, predicting public feedback is usually difficult before news is released. To tackle these challenges, we introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. We develop a feedback simulation system that generates public feedback considering demographic distributions. Through extensive quantitative and qualitative evaluations, our system shows significant improvements in news-generating capabilities and verifies the effectiveness of public feedback simulation.
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