PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models

arXiv:2311.08590v2 Announce Type: replace
Abstract: Pre-trained language models (PLMs) show impressive performance in various downstream NLP tasks. However, pre-training large language models demands substantial memory and training compute. Furthermore, due to the substantial resources required, many PLM weights are confidential. Consequently, users are compelled to share their data with model owners for fine-tuning specific tasks. To overcome the limitations, we introduce Plug-in External Memory Adaptation (PEMA), a Parameter-Efficient Fine-Tuning (PEFT) method, enabling PLM fine-tuning without requiring access to all the …

abstract arxiv compute cs.cl data however language language models large language large language models memory nlp performance pre-training resources show tasks training type