测试大模型在小数据蛋白预测任务中的表现，发现效果显著。
Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark
- 用FLIP基准评估ESM-2和SaProt在小样本场景下的表现
- 大模型在数据有限时仍保持较高预测性能
- 适合研究小样本蛋白功能预测的学者参考
本研究扩展了FLIP基准——一个专为评估蛋白适应性预测模型在小规模、特定预测任务中表现而设计的基准——以评估当前最先进的大型蛋白语言模型（包括ESM-2和SaProt）在该数据集上的性能。与覆盖广泛任务的ProteinGym等大型多样基准不同，FLIP聚焦于数据稀缺的受限场景，因此成为评估模型在任务数据有限条件下的表现的理想框架。我们探究了近期蛋白语言模型的发展是否能在这些条件下带来显著提升。研究结果为大规模模型在特定蛋白预测任务中的表现提供了宝贵洞见。
原文摘要 · Abstract (English)
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.
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