Prompt Based Fine Tuning

Prompt-based fine-tuning is a parameter-efficient approach to adapting pre-trained language models (and increasingly, multimodal models) to specific downstream tasks by providing carefully crafted prompts as input. Current research focuses on optimizing prompt design, exploring efficient algorithms like graph neural networks to guide information flow within the model, and developing methods to mitigate biases and improve performance in low-resource or cross-lingual scenarios. This technique offers a significant advantage by reducing computational costs and memory requirements compared to full model fine-tuning, making advanced language models more accessible for diverse applications and research areas.