In the rapidly advancing domain of artificial intelligence, state-of-the-art language models such as OpenAI's GPT-3.5-turbo and GPT-4 offer unprecedented opportunities for automating complex tasks. This research paper delves into the capabilities of these models for semantically analyzing corporate disclosures in the Korean context, specifically for timely disclosure. The study focuses on the top 50 publicly traded companies listed on the Korean KOSPI, based on market capitalization, and scrutinizes their monthly disclosure summaries over a period of 17 months. Each summary was assigned a sentiment rating on a scale ranging from 1(very negative) to 5(very positive). To gauge the effectiveness of the language models, their sentiment ratings were compared with those generated by human experts. Our findings reveal a notable performance disparity between GPT-3.5-turbo and GPT-4, with the latter demonstrating significant accuracy in human evaluation tests. The Spearman correlation coefficient was registered at 0.61, while the simple concordance rate was recorded at 0.82. This research contributes valuable insights into the evaluative characteristics of GPT models, thereby laying the groundwork for future innovations in the field of automated semantic monitoring.

OpenAI的GPT-3.5-turbo和GPT-4等最先进的语言模型在人工智能领域提供了自动化复杂任务的前所未有的机会。本研究将这些模型的能力应用于韩国背景下企业信息披露的语义分析，尤其关注韩国KOSPI上市的市值前50家公司的月度披露摘要。通过将每个摘要分配到1（非常负面）到5（非常正面）的情感评分，以人工专家所生成的评分与语言模型生成的评分进行对比，研究发现了GPT-3.5-turbo和GPT-4之间明显的性能差异，后者在人工评价测试中表现出了显著的准确性。Spearman相关系数为0.61，简单一致性率为0.82。该研究为GPT模型的评估特性提供了宝贵的见解，为自动语义监控领域的未来创新奠定了基础。

大型语言模型用于企业披露的语义监控：韩国前50家KOSPI公司的案例研究