标题： 金融领域的大语言模型：什么是金融情感？ 显示英文标题

标题： Large language models in finance : what is financial sentiment?

摘要： 金融情绪已成为金融领域一个至关重要的复杂概念，在市场预测和投资策略中使用得越来越多。 尽管其重要性日益增加，但仍然需要定义和理解金融情绪真正代表什么，以及如何有效衡量它。 我们探讨了金融情绪的本质，并研究了大型语言模型（LLMs）如何对其做出估算。 我们追踪了金融领域情绪测量的发展历程，从基于市场的和基于词典的方法到先进的自然语言处理技术。 LLMs的出现显著增强了情绪分析，提供了更深层次的上下文理解和从金融文本中提取情绪时更高的准确性。 我们考察了基于BERT的模型（如RoBERTa和FinBERT）如何被优化用于结构化情绪分类，而基于GPT的模型（包括GPT-4、OPT和LLaMA）则在金融文本生成和实时情绪解读方面表现出色。 双向和自回归变压器架构的对比分析突显了它们在投资者情绪分析、算法交易和金融决策中的各自作用。 通过探讨金融情绪是什么以及LLMs如何估算它，我们提供了关于AI驱动的情绪分析在金融领域日益增长的作用的见解。 显示英文摘要

摘要： Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand what financial sentiment truly represents and how it can be effectively measured. We explore the nature of financial sentiment and investigate how large language models (LLMs) contribute to its estimation. We trace the evolution of sentiment measurement in finance, from market-based and lexicon-based methods to advanced natural language processing techniques. The emergence of LLMs has significantly enhanced sentiment analysis, providing deeper contextual understanding and greater accuracy in extracting sentiment from financial text. We examine how BERT-based models, such as RoBERTa and FinBERT, are optimized for structured sentiment classification, while GPT-based models, including GPT-4, OPT, and LLaMA, excel in financial text generation and real-time sentiment interpretation. A comparative analysis of bidirectional and autoregressive transformer architectures highlights their respective roles in investor sentiment analysis, algorithmic trading, and financial decision-making. By exploring what financial sentiment is and how it is estimated within LLMs, we provide insights into the growing role of AI-driven sentiment analysis in finance.