Abstract
Large language models (LLMs) play an increasingly important role in finan- cial markets analysis by capturing signals from complex and heterogeneous textual data sources, such as tweets, news articles, reports, and microblogs. However, their performance is dependent on large computational resources and proprietary datasets, which are costly, restricted, and therefore inacces- sible to many researchers and practitioners. To reflect realistic situations we investigate the ability of lightweight open-source LLMs - smaller and publicly available models designed to operate with limited computational resources - to generalize sentiment understanding from financial datasets of varying sizes, sources, formats, and languages. We compare the benchmark finance natural language processing (NLP) model, FinBERT, and three open-source lightweight LLMs, DeepSeek-LLM 7B, Llama3 8B Instruct, and Qwen3 8B on five publicly available datasets: FinancialPhraseBank, Financial Question Answering, Gold News Sentiment, Twitter Sentiment and Chinese Finance Sentiment. We find that LLMs, specially Qwen3 8B and Llama3 8B, perform best in most scenarios, even from using only 5% of the available training data. These results hold in zero-shot and few-shot learning scenarios. Our findings indicate that lightweight, open-source large language models (LLMs) consti- tute a cost-effective option, as they can achieve competitive performance on heterogeneous textual data even when trained on only a limited subset of the extensive annotated corpora that are typically deemed necessary.
Abstract (translated)
大型语言模型（LLM）在金融市场分析中扮演着越来越重要的角色，通过捕捉来自复杂且异质化的文本数据源（如推特、新闻文章、报告和微博等）的信号来发挥作用。然而，其性能依赖于大规模计算资源和专有数据集，这些资源成本高昂且受到限制，因此许多研究人员和从业者无法获取。为了反映实际情况，我们研究了轻量级开源LLM的能力——即那些设计用于有限计算资源的小型公开模型，在不同大小、来源、格式和语言的金融数据集中进行情感理解泛化的能力。我们在五个公共数据集上对基准金融自然语言处理（NLP）模型FinBERT以及三个开源轻量级LLM进行了比较，这些数据集包括FinancialPhraseBank、FinancialQuestionAnswering、GoldNewsSentiment、TwitterSentiment和ChineseFinanceSentiment。我们发现，在大多数情况下，特别是使用仅5%的可用训练数据时，LLMs表现最佳，尤其是Qwen3 8B和Llama3 8B。在零样本学习（zero-shot）和少量样例学习（few-shot）场景中，这些结果仍然成立。我们的研究结果表明，轻量级开源大型语言模型构成了一种成本效益高的选择，即使在训练数据仅限于通常被认为必要的广泛注释语料库的有限子集时，它们也能实现与同类模型相竞争的表现。
URL
https://arxiv.org/abs/2512.00946
