In the following section, we will be talking about soft prompts and interpretable soft prompts. This is a step up in difficulty as it involves LLM concepts that we have not covered prior. Soft prompts involve a concept called Prompt Tuning, which freezes the LLM's weights and instead modifies the input. It is important to recognize that this is a concept discussed previously and offers a novel approach to leveraging LLMs.
By the end of this section, you will have an understanding of how Prompt Tuning works and how soft prompts can be used to enhance the performance and interpretability of your GenAI applications.
Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.