AI-Press：一个由大语言模型驱动的多智能体新闻生成与反馈模拟系统
AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models
AI总结：
针对大语言模型新闻生成的专业性、伦理局限及发布前公众反馈难预测问题，研究提出基于多智能体协作与检索增强生成的AI-Press系统，搭配人口分布驱动的反馈模拟功能，经评估新闻生成能力显著提升，反馈模拟有效。
摘要：
各类社交平台的兴起改变了新闻行业。由于新闻内容需求不断增长，大语言模型（LLM）凭借其速度和成本效益，在新闻生产中的应用日益增多。然而，LLM在新闻生成方面仍存在专业性和伦理判断上的局限。此外，在新闻发布前预测公众反馈通常较为困难。为应对这些挑战，我们推出了AI-Press，这是一个基于多智能体协作与检索增强生成的自动化新闻撰稿与润色系统。我们开发了一套反馈模拟系统，可结合人口统计分布生成公众反馈。通过大量定量与定性评估，我们的系统在新闻生成能力上展现出显著提升，并验证了公众反馈模拟的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
