Is it true that the larger the number of parameters, the higher the accuracy?

Not necessarily. While increasing parameters can improve model capacity and accuracy up to a point, it is not an absolute guarantee of better performance.

Key factors dictate whether more parameters translate to higher accuracy: the quality and volume of training data must be sufficient; the model architecture must effectively utilize the added capacity; careful regularization is essential to prevent overfitting; and benefits eventually reach diminishing returns. Pushing parameters beyond what the problem complexity requires often wastes resources and can harm performance without adequate data and tuning.

In practice, scaling parameters significantly boosts accuracy for complex tasks like image recognition or language modeling when paired with massive, high-quality datasets and sufficient compute resources. However, smaller, well-designed models often achieve comparable accuracy on simpler tasks or limited data, offering better cost-effectiveness and deployment efficiency. Engineers must balance parameter size against data availability and practical constraints.