标题： 金融命名实体识别：大模型能走多远？ 显示英文标题

标题： Financial Named Entity Recognition: How Far Can LLM Go?

摘要： 大型语言模型（LLMs）的兴起彻底改变了从日益增长的财务报表、公告和商业新闻中提取和分析关键信息的方式。对命名实体进行识别以构建结构化数据在分析财务文档中是一个重大挑战，也是智能财务分析的基础任务。然而，这些通用LLMs的效果及其在不同提示下的表现仍需要更好的理解。为了填补这一空白，我们对金融命名实体识别（NER）问题中的最先进LLMs和提示方法进行了系统评估。具体而言，我们的实验结果突出了它们的优势和局限性，识别出五种典型的失败类型，并提供了它们在特定领域任务中的潜力和挑战的见解。 显示英文摘要

摘要： The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight their strengths and limitations, identify five representative failure types, and provide insights into their potential and challenges for domain-specific tasks.