Conditions under which one protein language model outperforms another

Determine the specific combinations of protein language model architecture, pre-training dataset size, and dataset distribution that lead one protein language model to outperform another.

Background

Protein LLM development involves multiple high-cost design choices, including architecture selection, pre-training dataset size, and dataset distribution. Despite extensive empirical progress and scaling, clear criteria for how these factors interact to produce superior performance remain unspecified.

Establishing these conditions would provide actionable guidance for model design and resource allocation, reducing development costs and improving reproducibility and comparability across models trained under different configurations.