Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark

TL;DR

This paper evaluates large protein language models like ESM-2 and SaProt on the FLIP benchmark, focusing on their effectiveness in small, data-scarce protein prediction tasks to understand their capabilities in constrained settings.

Contribution

It introduces an assessment of large protein language models on the FLIP benchmark, highlighting their performance in limited-data scenarios, which was not extensively studied before.

Findings

Large models show improved performance in constrained settings

Performance gains are more pronounced with increased model size

Insights into model suitability for specialized protein tasks

Abstract

In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as ProteinGym, which cover a broad spectrum of tasks, FLIP focuses on constrained settings where data availability is limited. This makes it an ideal framework to evaluate model performance in scenarios with scarce task-specific data. We investigate whether recent advances in protein language models lead to significant improvements in such settings. Our findings provide valuable insights into the performance of large-scale models in specialized protein prediction tasks.