Part 3/11:

The answer is not straightforward. Researchers and practitioners often face the dilemma of trial and error, incurring considerable costs in terms of time, computational resources, and finances to empirically test multiple models. This process is inefficient and resource-intensive, especially as the model pool continues to grow.

Challenges in Model Selection and Fine-Tuning

The core issues highlighted include:

• Diverse and complex models: Each with unique strengths and weaknesses contingent upon the task.

• Data quality and domain specificity: Effective fine-tuning depends heavily on high-quality, domain-relevant data.

• Resource constraints: Large models require significant infrastructure, financial costs, and expertise.