prompt tuning

prompt tuning 

Prompt tuning is the process of feeding front-end prompts into your AI model in the context of a specific task. These prompts could be anything – just some extra words introduced by a human or AI-generated number introduced in the embedding layers. Prompt tuning is used to guide a model toward a particular prediction. With prompt tuning, organizations with limited data can tailor a model to perform a slick task while eliminating the requirement to update the model billions or weights. 

The journey of prompt-tuning began with large language models and gradually expanded to foundation models like transformers that take care of sequential data types, including audio and video. Prompts can mean a number of things like streams of speech, a still image or video, or snippets of text. 

Way before prompt-tuning became a reality, there was another term called prompt engineering which meant prompts that were designed by hand. 

Prompt engineering came into force when OpenAi’s ambitious GPT (Generative Pretrained Transformer), a language model almost 10 times bigger than any of its predecessors, was released.

OpenAI researchers revealed that GPT’s successor, GPT-3 successor, at 175 billion parameters, enabled it to perform specialized tasks with only a few words introduced at inference time. In this case, where there was no retraining involved, GPT-3 performed equally well as a model fine-tuned on labeled data. 

And soon, the hand-crafted prompts saw their way out of the system and were replaced by superior AI-designed prompts which comprised strings of numbers. A year later, Google researchers formally introduced the Ai-designed “soft” prompts which performed way better than the human-engineered “hard” prompts.

While prompt tuning was still under review, Stanford researchers announced prefix-tuning, which was another automated prompt-design method that empowered the model to consecutively learn tasks. Prefix-tuning was a combination of soft prompts and prompts that are fed into the layers of the deep learning model to enhance flexibility. Though prompt tuning is considered more efficient, the good part about both techniques is that you can freeze the model and do away with the expensive retraining.





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