Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?

• URL: http://arxiv.org/abs/2503.04873v1
• Date: Thu, 06 Mar 2025 16:38:12 GMT
• Title: Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?
• Authors: Xinyu Wei, Luojia Liu,
• Abstract summary: Recently, large language models (FSAMs) with hundreds of billions of parameters have demonstrated the emergent ability to explore domain-specific data methods.<n>In this paper, we aim to answer the fundamental question: whether this question can yield informative insights on whether LLMs learn can address the challenges by general in-context demonstrations of document-sentiment pairs to the sentiment analysis of new documents.
• Score: 0.6813925418351435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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