Comparing LLMs for Sentiment Analysis in Financial Market News
Lucas Eduardo Pereira Teles and Carlos M. S. Figueiredo
本文介绍了大型语言模型(LLM)在金融市场新闻情绪分析任务中的比较研究。 这项工作旨在分析这些模型在财务背景下这一重要的自然语言处理任务的性能差异。 LLM模型与经典方法进行比较,允许量化每个测试模型或方法的好处。 结果表明,大型语言模型在绝大多数情况下优于经典模型。
This article presents a comparative study of large language models (LLMs) in the task of sentiment analysis of financial market news. This work aims to analyze the performance difference of these models in this important natural language processing task within the context of finance. LLM models are compared with classical approaches, allowing for the quantification of the benefits of each tested model or approach. Results show that large language models outperform classical models in the vast ma...
