大模型能在不微调情况下，通过上下文示例学会金融情感分析。
Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?
- 用金融文本-情感配对示例引导大模型进行上下文学习。
- 多数现代大模型在金融情感分析上表现良好，无需领域微调。
- 适合想快速部署金融情绪分析系统的开发者参考。
近期，参数量达数百亿的大语言模型（LLMs）在多个领域展现出超越传统方法的涌现能力，即使未在特定领域数据上微调也表现优异。然而，在金融情感分析（FSA）这一金融AI基础任务中，这些模型常面临复杂术语、主观情绪及表达模糊等挑战。本文旨在回答核心问题：大模型是否适合作为金融情感分析的上下文学习者？揭示此问题可提供重要洞见——大模型能否通过泛化金融文档与情感配对的上下文示例，完成新文档的情感分析，而无需对金融数据进行微调。据我们所知，这是首个系统探索大模型在金融情感分析中上下文学习能力的研究，覆盖了多数现代大模型（包括最新发布的DeepSeek V3）及多种上下文样本选择方法。全面实验验证了大模型在金融情感分析中的上下文学习潜力。
原文摘要 · Abstract (English)
Recently, large language models (LLMs) with hundreds of billions of parameters have demonstrated the emergent ability, surpassing traditional methods in various domains even without fine-tuning over domain-specific data. However, when it comes to financial sentiment analysis (FSA)$\unicode{x2013}$a fundamental task in financial AI$\unicode{x2013}$these models often encounter various challenges, such as complex financial terminology, subjective human emotions, and ambiguous inclination expressions. In this paper, we aim to answer the fundamental question: whether LLMs are good in-context learners for FSA? Unveiling this question can yield informative insights on whether LLMs can learn to address the challenges by generalizing in-context demonstrations of financial document-sentiment pairs to the sentiment analysis of new documents, given that finetuning these models on finance-specific data is difficult, if not impossible at all. To the best of our knowledge, this is the first paper exploring in-context learning for FSA that covers most modern LLMs (recently released DeepSeek V3 included) and multiple in-context sample selection methods. Comprehensive experiments validate the in-context learning capability of LLMs for FSA.
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