Why do models with larger parameter counts perform better?

Models with larger parameter counts generally achieve superior performance because they possess a greater capacity to learn intricate patterns and complex representations within the data. This enables them to model sophisticated relationships between inputs and outputs.

Key principles include increased model capacity for representing complex functions, improved ability to disentangle and combine features hierarchically, and finer-grained pattern recognition across vast datasets. However, this requires significantly more high-quality training data and vastly increased computational resources for training and deployment. Performance gains often follow a power law relative to scale, but can exhibit diminishing returns beyond a data-constrained threshold. Larger models are computationally expensive and require specialized hardware.

Increased scale drives breakthroughs in complex tasks like natural language understanding, intricate computer vision problems, and multimodal integration. Larger models achieve state-of-the-art results on benchmarks by capturing subtle nuances, long-range dependencies, and transfer learning capabilities, fueling advancements in applications such as advanced chatbots, detailed image generation, and sophisticated translation systems.