Can prompt engineering methods surpass fine-tuning performance with pre-trained large language models?
The pre-training and fine-tuning paradigm in natural language processing led to state-of- the-art (SOTA) performance in downstream tasks. To serve a wide range of downstream tasks, pre-trained models must be fine-tuned on task-specific datasets and the resulting models must be served at inference time. …
Can prompt engineering methods surpass fine-tuning performance with pre-trained large language models? The pre-training and fine-tuning paradigm in natural language processing led to state-of- the-art (SOTA) performance in downstream tasks. To serve a wide range of downstream tasks, pre-trained models must be fine-tuned on task-specific datasets and the resulting models must be served at inference time. … nlp
prompt engineering method surpass finetuning performance pretrained large language model pretraining finetuning paradigm natural language processing led stateof theart SOTA performance downstream task serve wide range downstream task pretrained model must finetuned taskspecific datasets resulting model must served inference time … nlp