Large language models (LLMs) have garnered significant interest in natural language processing (NLP), particularly their remarkable performance in various downstream tasks in resource-rich languages. Recent studies have highlighted the limitations of LLMs in low-resource languages, primarily focusing on binary classification tasks and giving minimal attention to South Asian languages. These limitations are primarily attributed to constraints such as dataset scarcity, computational costs, and research gaps specific to low-resource languages. To address this gap, we present datasets for sentiment and hate speech tasks by translating from English to Bangla, Hindi, and Urdu, facilitating research in low-resource language processing. Further, we comprehensively examine zero-shot learning using multiple LLMs in English and widely spoken South Asian languages. Our findings indicate that GPT-4 consistently outperforms Llama 2 and Gemini, with English consistently demonstrating superior performance across diverse tasks compared to low-resource languages. Furthermore, our analysis reveals that natural language inference (NLI) exhibits the highest performance among the evaluated tasks, with GPT-4 demonstrating superior capabilities.

本研究解决了大型语言模型在低资源语言中的表现不足，尤其是南亚语言的研究空白。通过翻译创建针对情感和仇恨言论任务的数据集，并对多种大型语言模型进行了零-shot 学习的比较。研究发现，GPT-4在多个任务中性能优于其他模型，并且自然语言推理任务表现最佳，彰显了其在低资源语言处理中的重要性。

大型语言模型是否平等对待所有语言？在低资源环境中的比较研究