Optimizing NLP Model Deployment: A Comparative Analysis of Fine-Tuning, Few-Shot Learning, and Prompt Engineering Strategies

As large language models continue to revolutionize natural language processing, the trade-offs between performance, computational efficiency, and resource requirements become increasingly critical. This paper presents a comprehensive evaluation of three prominent approaches: fine-tuning, few-shot learning, and zero-shot learning using sentiment analysis as a case study. We benchmark the efficacy of these methods across multiple dimensions, including accuracy, inference time, computational cost, and adaptability to new domains. Our experiments, conducted on a state-of-the-art NVIDIA H100 GPU and extrapolated to AWS cloud infrastructure, reveal nuanced insights into the practical implications of deploying these approaches. Fine-tuning achieved the highest accuracy (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 0. 0 6 \%}$</tex>) but at substantial computational cost (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$344.10$</tex> for 5 iterations). Few-shot learning emerged as a compelling alternative, reaching 65.05 % accuracy with minimal resource usage (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$17.95$</tex> for 5 iterations) and demonstrating strong adaptability. Surprisingly, zero-shot learning with prompts showed decreased performance compared to no-prompt baselines, challenging common assumptions about prompt engineering while using a fine tuned model. These findings have far-reaching implications for the deployment of language models across various applications. We provide a decision-making framework that balances performance gains against resource constraints, offering valuable guidance for practitioners and researchers alike. Our research not only contributes to the ongoing dialogue about the most effective ways to leverage large language models but also highlights the need for continued innovation in resource-efficient NLP techniques. As the field evolves, the insights from this study will help inform strategies for developing more adaptable, efficient, and powerful language processing systems.