| Summary: | The rapid proliferation of large language models (LLMs) has led to an intense focus on achieving unprecedented performance benchmarks, often at the expense of considering the substantial computational costs involved. This oversight is compounded by the lack of robust, academically grounded frameworks for comprehensively evaluating these costs, their sources, and strategies for minimization while balancing performance imperatives. To address this critical gap, my research aims to develop a rigorous and systematic framework that enables researchers and industry stakeholders to understand and contextualize the cost implications of fine-tuning, prompt engineering, and prompt automation techniques. By offering a systematic approach to evaluating the trade-offs between performance, cost, and societal impact, this research seeks to advance the practical and sustainable adoption of LLMs across diverse applications.
|
|---|