Zero- And Few-shot Prompting With Llms: A Comparative Study With Fine-tuned Models For Bangla Sentiment Analysis

Hasan Md. Arid, Das Shudipta, Anjum Afiyat, Alam Firoj, Anjum Anika, Sarker Avijit, Noori Sheak Rashed Haider. Arxiv 2023

[Paper]    
Applications Few Shot Fine Tuning GPT In Context Learning Model Architecture Pretraining Methods Prompting Reinforcement Learning Tools Transformer

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.

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