面向金融情感分析的微调大语言模型与大语言模型少样本学习的对比分析
A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis
AI总结：
该研究针对大语言模型在金融情感分析中的应用缺口，对比了上下文学习与小参数量大语言模型微调两种方案，发现小模型微调及零/单样本学习均可达到SOTA水平，且增加少样本数量无性能增益。
摘要：
金融情感分析在挖掘潜在模式、识别新兴趋势方面发挥着关键作用，能帮助人们做出明智决策，在瞬息万变的金融领域中获得显著优势。近年来，大语言模型（LLMs）已在多个领域展现出有效性，即便在各类自然语言处理（NLP）任务的零样本和少样本上下文学习场景中也表现出卓越能力。然而，其在金融情感分析场景下的潜力与适用性尚未得到充分探索。为填补这一空白，我们采用两种方法：上下文学习（重点研究gpt-3.5-turbo模型）以及在金融领域数据集上微调大语言模型。考虑到大参数量大语言模型的微调计算成本较高，我们聚焦于参数量在2.5亿到30亿之间的较小规模大语言模型开展微调实验。随后我们将其性能与当前最优（SOTA）结果进行对比，以评估它们在金融领域的有效性。我们的结果表明，经过微调的较小规模大语言模型，即便参数量更少、训练数据集规模更小，也能达到与当前最优微调大语言模型相当的性能。此外，大语言模型的零样本和单样本性能，也能取得与微调后的较小规模大语言模型及当前最优结果相当的表现。进一步分析显示，在金融领域情感分析任务中，增加上下文学习的样本数量并未观测到性能提升。
英文摘要：
Financial sentiment analysis plays a crucial role in uncovering latent patterns and detecting emerging trends, enabling individuals to make well-informed decisions that may yield substantial advantages within the constantly changing realm of finance. Recently, Large Language Models (LLMs) have demonstrated their effectiveness in diverse domains, showcasing remarkable capabilities even in zero-shot and few-shot in-context learning for various Natural Language Processing (NLP) tasks. Nevertheless, their potential and applicability in the context of financial sentiment analysis have not been thoroughly 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 LLMs on a finance-domain dataset. Given the computational costs associated with fine-tuning LLMs with large parameter sizes, our focus lies on smaller LLMs, spanning from 250M to 3B parameters for fine-tuning. We then compare the performances with state-of-the-art results to evaluate their effectiveness in the finance-domain. Our results demonstrate that fine-tuned smaller LLMs can achieve comparable performance to state-of-the-art fine-tuned LLMs, even with models having fewer parameters and a smaller training dataset. Additionally, the zero-shot and one-shot performance of LLMs produces comparable results with fine-tuned smaller LLMs and state-of-the-art outcomes. Furthermore, our analysis demonstrates that there is no observed enhancement in performance for finance-domain sentiment analysis when the number of shots for in-context learning is increased.
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