Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks due to their commendable zero-shot abilities. Yet, directly applying LLMs to financial sentiment analysis presents challenges: The discrepancy between the pre-training objective of LLMs and predicting the sentiment label can compromise their predictive performance. Furthermore, the succinct nature of financial news, often devoid of sufficient context, can significantly diminish the reliability of LLMs' sentiment analysis. To address these challenges, we introduce a retrieval-augmented LLMs framework for financial sentiment analysis. This framework includes an instruction-tuned LLMs module, which ensures LLMs behave as predictors of sentiment labels, and a retrieval-augmentation module which retrieves additional context from reliable external sources. Benchmarked against traditional models and LLMs like ChatGPT and LLaMA, our approach achieves 15\% to 48\% performance gain in accuracy and F1 score.

翻译：金融情感分析对于估值和投资决策至关重要。然而，传统自然语言处理模型受限于其参数量和训练数据集的规模，这削弱了其泛化能力和在该领域的有效性。近年来，在海量语料库上预训练的大语言模型凭借其出色的零样本能力，在各种自然语言处理任务中表现出卓越性能。但将大语言模型直接应用于金融情感分析仍面临挑战：大语言模型的预训练目标与情感标签预测之间的差异会降低其预测性能。此外，金融新闻通常简洁且缺乏足够的上下文信息，这显著削弱了大语言模型情感分析的可靠性。为解决这些问题，我们提出了一种面向金融情感分析的检索增强型大语言模型框架。该框架包含指令微调的大语言模型模块（确保大语言模型充当情感标签预测器）和检索增强模块（从可靠外部来源检索附加上下文）。在ChatGPT和LLaMA等大语言模型及传统模型的基准测试中，我们的方法在准确率和F1分数上实现了15%至48%的性能提升。