The paper "Parameter-Efficient Transfer Learning for NLP" by Neil Houlsby et al. addresses the challenge of fine-tuning large pre-trained models in natural language processing (NLP) for multiple downstream tasks. Fine-tuning is effective but inefficient in terms of parameters, requiring a new model for each task. The authors propose using adapter modules, which are compact and extensible, adding only a few trainable parameters per task. This approach allows for incremental learning without revisiting previous tasks, while maintaining high parameter sharing. The effectiveness of adapters is demonstrated through experiments on 26 diverse text classification tasks, including the GLUE benchmark. Adapters achieve near-state-of-the-art performance with significantly fewer parameters compared to full fine-tuning. The method is also shown to work well on other tasks like SQuAD extractive question answering, further validating its parameter efficiency and performance. The key innovation lies in the design of an effective adapter module and its integration with the base model, achieving a balance between performance and parameter efficiency.The paper "Parameter-Efficient Transfer Learning for NLP" by Neil Houlsby et al. addresses the challenge of fine-tuning large pre-trained models in natural language processing (NLP) for multiple downstream tasks. Fine-tuning is effective but inefficient in terms of parameters, requiring a new model for each task. The authors propose using adapter modules, which are compact and extensible, adding only a few trainable parameters per task. This approach allows for incremental learning without revisiting previous tasks, while maintaining high parameter sharing. The effectiveness of adapters is demonstrated through experiments on 26 diverse text classification tasks, including the GLUE benchmark. Adapters achieve near-state-of-the-art performance with significantly fewer parameters compared to full fine-tuning. The method is also shown to work well on other tasks like SQuAD extractive question answering, further validating its parameter efficiency and performance. The key innovation lies in the design of an effective adapter module and its integration with the base model, achieving a balance between performance and parameter efficiency.