基于LLM的零样本与少样本提示：与微调模型在孟加拉语情感分析中的比较研究
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis
AI总结：
本文构建了包含33,606条孟加拉语文本的情感分析数据集，并比较零样本、少样本LLM提示与微调模型，发现单语Transformer模型表现最优。
摘要：
数字世界的快速扩展使情感分析成为市场营销、政治、客户服务和医疗保健等多个领域的关键工具。尽管在广泛使用的语言中情感分析已取得显著进展，但像孟加拉语这样的低资源语言由于资源限制，在很大程度上仍未得到充分研究。此外，大型语言模型（LLMs）近期在各种应用中展现出前所未有的性能，这凸显了在低资源语言背景下评估它们的必要性。在本研究中，我们提出了一个规模可观的人工标注数据集，包含33,606条孟加拉语新闻推文和Facebook评论。我们还研究了包括Flan-T5、GPT-4和Bloomz在内的多个语言模型的零样本和少样本上下文学习，并与微调模型进行了比较分析。我们的研究结果表明，基于单语Transformer的模型即使在零样本和少样本场景中也始终优于其他模型。为促进持续探索，我们打算将该数据集和我们的研究工具公开发布给更广泛的研究社区。
英文摘要：
The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,606 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
