The paper "Fine-tuning protein language models boosts predictions across diverse tasks" by Robert Schmirlert, Michael Heinzinger, and Burkhard Rost explores the effectiveness of fine-tuning protein language models (pLMs) on various protein prediction tasks. The authors compare the performance of three state-of-the-art pLMs (ESM2, ProtTS, and Ankh) on eight different tasks, finding that task-specific supervised fine-tuning almost always improves downstream predictions. They also demonstrate that parameter-efficient fine-tuning can achieve similar improvements with significantly fewer resources, up to 4.5 times faster training compared to fine-tuning full models. The study suggests that fine-tuning should be a standard practice, especially for small datasets, and provides easy-to-use notebooks to facilitate the process. The results highlight the benefits of fine-tuning in improving prediction accuracy and efficiency, particularly for tasks such as disorder prediction and mutational landscape analysis.The paper "Fine-tuning protein language models boosts predictions across diverse tasks" by Robert Schmirlert, Michael Heinzinger, and Burkhard Rost explores the effectiveness of fine-tuning protein language models (pLMs) on various protein prediction tasks. The authors compare the performance of three state-of-the-art pLMs (ESM2, ProtTS, and Ankh) on eight different tasks, finding that task-specific supervised fine-tuning almost always improves downstream predictions. They also demonstrate that parameter-efficient fine-tuning can achieve similar improvements with significantly fewer resources, up to 4.5 times faster training compared to fine-tuning full models. The study suggests that fine-tuning should be a standard practice, especially for small datasets, and provides easy-to-use notebooks to facilitate the process. The results highlight the benefits of fine-tuning in improving prediction accuracy and efficiency, particularly for tasks such as disorder prediction and mutational landscape analysis.