Continuous Prompt

Continuous prompting is a technique in natural language processing that involves using continuously-valued vectors, rather than discrete text, to guide large language models (LLMs) towards specific tasks. Current research focuses on improving the efficiency and interpretability of these prompts, exploring methods like progressive fine-tuning to internalize prompt knowledge within the model and developing techniques to generate continuous prompts from discrete embeddings or transfer them between models. This approach offers significant potential for reducing inference costs, enhancing model performance on various tasks (including image generation and medical applications), and improving the robustness and security of LLMs, but challenges remain in understanding and mitigating unexpected behaviors and vulnerabilities.