Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models

arXiv:2404.14850v1 Announce Type: cross
Abstract: Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing Parameter-Efficient Fine-Tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is non-trivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable …

adapter arxiv cs.cl cs.lg language language models protein q-bio.bm scalable simple type