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Financial sentiment analysis plays a crucial role in uncovering latentpatterns and detecting emerging trends, enabling individuals to makewell-informed decisions that may yield substantial advantages within theconstantly changing realm of finance. Recently, Large Language Models (LLMs)have demonstrated their effectiveness in diverse domains, showcasing remarkablecapabilities even in zero-shot and few-shot in-context learning for variousNatural Language Processing (NLP) tasks. Nevertheless, their potential andapplicability in the context of financial sentiment analysis have not beenthoroughly explored yet. To bridge this gap, we employ two approaches:in-context learning (with a focus on gpt-3.5-turbo model) and fine-tuning LLMson a finance-domain dataset. Given the computational costs associated withfine-tuning LLMs with large parameter sizes, our focus lies on smaller LLMs,spanning from 250M to 3B parameters for fine-tuning. We then compare theperformances with state-of-the-art results to evaluate their effectiveness inthe finance-domain. Our results demonstrate that fine-tuned smaller LLMs canachieve comparable performance to state-of-the-art fine-tuned LLMs, even withmodels having fewer parameters and a smaller training dataset. Additionally,the zero-shot and one-shot performance of LLMs produces comparable results withfine-tuned smaller LLMs and state-of-the-art outcomes. Furthermore, ouranalysis demonstrates that there is no observed enhancement in performance forfinance-domain sentiment analysis when the number of shots for in-contextlearning is increased.

A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis