对比大模型在金融新闻情感分析中的表现，发现大模型普遍更优。
Comparing LLMs for Sentiment Analysis in Financial Market News
- 用经典方法与大模型对比分析金融新闻情感
- 大模型在多数情况下显著优于传统方法
- 适合关注金融文本分析的从业者参考
本文对大型语言模型（LLMs）在金融市场新闻情感分析任务中的表现进行了比较研究。该工作旨在分析这些模型在金融领域这一重要自然语言处理任务中的性能差异。通过与经典方法进行对比，量化了每种模型或方法的优势。结果显示，大型语言模型在绝大多数情况下均优于传统模型。
原文摘要 · Abstract (English)
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 majority of cases.
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