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

2025-01-30

AI Summary

Existing evaluations of large protein language models (e.g., ESM-2, SaProt) rely predominantly on broad benchmarks (e.g., ProteinGym), overlooking their performance in realistic, low-data, task-specific scenarios—particularly few-shot fitness prediction under the FLIP benchmark. Method: This work introduces the first standardized evaluation framework for zero-shot and few-shot transfer to fitness prediction, enabling systematic cross-model comparison on FLIP. Contribution/Results: Empirical results reveal limited performance gains for current large models under stringent data constraints, exposing fundamental bottlenecks in few-shot protein modeling. The study delineates the practical applicability boundaries of large protein language models in low-resource settings and provides empirical grounding for lightweight adaptation strategies and paradigm shifts in evaluation methodology. These findings offer critical guidance for designing efficient, deployable protein AI models tailored to real-world experimental constraints.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Protein Language Models
• Limited Data
• Prediction Accuracy

Innovation

• FLIP Test Expansion
• Protein Language Models
• Limited Data Performance