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In the late 2010s, models like ELMo and BERT introduced fine-tuning: they allowed for the entire architecture of the pre-trained model to be reused and concatenated with a minimal number of additional weights for each task. In 2013, word2vec bundled general-purpose word embeddings into a static library people used them as off-the-shelf inputs to their NLP models. Throughout the years, we figured out ways of reusing more and more pre-trained weights when building task-specific models. One can argue that prompting is the newest and most extreme form of transfer learning: a mechanism that allows previously-trained model weights to be reused in a novel context. Writing effective prompts can require as much work as picking up a new hobby. In a way, prompting has democratized transfer learning, but has not yet made it effortless. Each request for an image can be seen as a new task to be accomplished by a model that was pre-trained on a vast amount of data. Prompting is the newest and most extreme form of transfer learning. This article discusses potential ways in which users and companies will address the challenge of prompt engineering or prompt design.
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However, art will not necessarily come for free: the burden will simply shift away from drawing or using complex graphic design software to crafting effective text prompts to control the whims of text-to-image models. The advent of text-conditioned image generation models like DALL♾ is undoubtedly going to change the traditional creative process. Text prompt: “a boy desperately typing on a laptop keyboard, frustrated face, visibly angry, cartoon style”. Illustration produced via Midjourney (generative AI).