Prompt engineering for low-resource languages

Large Language Models (LLMs) face significant challenges when dealing with low-resource languages due to limited training data, tokenization complexities, code-mixing, and cultural biases, which affect their ability to provide accurate translations and analyses for speakers of these languages. Prompt engineering emerges as a crucial solution to bridge this gap, with strategies like Chain-of-Translation Prompting (CoTR) and Code-Mixed Prompting showing promise in enhancing model performance. CoTR involves translating inputs into English before processing and translating back, which has reduced error rates notably in tasks like sentiment analysis. Code-Mixed Prompting addresses the linguistic diversity and script variations in code-mixed languages, using techniques like temperature optimization and structured prompts for better language identification. Few-shot and zero-shot learning, along with explicit instruction-based prompts, further assist LLMs in handling low-resource languages by providing structured examples and step-by-step instructions. Future developments aim to create more inclusive AI systems by building stronger links between research and practical applications, expanding datasets, and supporting community-driven data collection, ensuring that speakers of all languages can fully engage in the digital age.

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