Deep Prompt

Deep prompt tuning is a technique that modifies pre-trained language models by adding or adjusting small sets of parameters ("prompts") rather than retraining the entire model. This approach focuses on improving efficiency and reducing computational costs for various downstream tasks, including question answering, text-to-image generation, and recommendation systems. Current research explores different prompt generation methods, such as personalized automatic prompts and instance-wise prompts, aiming to optimize performance and address challenges like implicit prompts and hallucination. The resulting efficiency gains and potential for improved model adaptability make deep prompt tuning a significant advancement in natural language processing and related fields.