标题： DynamicBench：评估大型语言模型中的实时报告生成 显示英文标题

标题： DynamicBench: Evaluating Real-Time Report Generation in Large Language Models

摘要： 传统的大语言模型（LLMs）基准测试通常依赖于通过讲故事或表达观点进行的静态评估，这无法捕捉当代应用中实时信息处理的动态需求。 为解决这一限制，我们提出了DynamicBench，这是一个旨在评估LLMs存储和处理最新数据能力的基准测试。 DynamicBench采用双路径检索管道，结合网络搜索与本地报告数据库。 它需要特定领域的知识，确保在专业领域内生成准确的报告。 通过在提供或不提供外部文档的场景中评估模型，DynamicBench有效衡量了它们独立处理最新信息或利用上下文增强的能力。 此外，我们引入了一个先进的报告生成系统，能够有效地管理动态信息的综合。 我们的实验结果证实了我们方法的有效性，我们的方法在无文档和有文档的场景中分别以7.0%和5.8%的优势超越了GPT4o。 代码和数据将向公众开放。 显示英文摘要

摘要： Traditional benchmarks for large language models (LLMs) typically rely on static evaluations through storytelling or opinion expression, which fail to capture the dynamic requirements of real-time information processing in contemporary applications. To address this limitation, we present DynamicBench, a benchmark designed to evaluate the proficiency of LLMs in storing and processing up-to-the-minute data. DynamicBench utilizes a dual-path retrieval pipeline, integrating web searches with local report databases. It necessitates domain-specific knowledge, ensuring accurate responses report generation within specialized fields. By evaluating models in scenarios that either provide or withhold external documents, DynamicBench effectively measures their capability to independently process recent information or leverage contextual enhancements. Additionally, we introduce an advanced report generation system adept at managing dynamic information synthesis. Our experimental results confirm the efficacy of our approach, with our method achieving state-of-the-art performance, surpassing GPT4o in document-free and document-assisted scenarios by 7.0% and 5.8%, respectively. The code and data will be made publicly available.