A Comprehensive Evaluation of Llama 3 for Text Classification Tasks

Text classification (TC) is one of core element of natural language processing (NLP), becoming more important as volume of text data increases. It is widely used in applications such as sentiment analysis, and topic categorization. Large language models (LLMs) have recently shown strong performance in these tasks, even without task-specific training. In this study, we evaluate Llama 3 8B model for text classification using zero-shot, and few-shot methods across many datasets. This study evaluates Llama 3 8B results on different NLP downstream tasks. Our findings show that Llama 3 8B can perform competitively across different settings, especially when guided by well-designed prompts. This study highlights how decoder LLMs can be effectively applied to classification tasks and provides acceptable performance.