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Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis

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dc.contributor.author Hasan, Md. Arid
dc.contributor.author Das, Shudipta
dc.contributor.author Anjum, Afiyat
dc.contributor.author Alam, Firoj
dc.contributor.author Anjum, Anika
dc.contributor.author Sarker, Avijit
dc.contributor.author Sheak Rashed Haider
dc.contributor.author Noori, Sheak Rashed Haider
dc.date.accessioned 2024-09-30T09:49:31Z
dc.date.available 2024-09-30T09:49:31Z
dc.date.issued 2024-05-25
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/13473
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Healthcare en_US
dc.subject Sentiment analysis en_US
dc.subject Mining, sentiment en_US
dc.title Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis en_US
dc.type Article en_US


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