As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first layer. However, previous methods have mainly focused on the initialization of prompt embeddings. The strategy of training and utilizing prompt embeddings in a reasonable way has become a limiting factor in the effectiveness of prompt tuning. To address this issue, we introduce the Global Prompt Cell (GPC), a portable control module for prompt tuning that selectively preserves prompt information across all encoder layers. Our experimental results demonstrate a 5.8% improvement on SuperGLUE datasets compared to vanilla prompt tuning.

Through The advent of instruction-following large language this process, we collect over 8,000 instruction models (LLMs), representative by Chat-data for supervised fine-tuning. Our model builds GPT(OpenAI, 2022), has generated significant interest upon the open-source LLaMa-7B base model, integrates due to their exceptional performance in understanding structured and unstructured medical knowledge instructions and generating human-like from the Chinese medical knowledge graph responses. Compared to smaller models, LLMs (CMeKG), and employs knowledge-based instruction exhibit strong generalization across various natural data for fine-tuning.

Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.