Memory-assisted prompt editing to improve GPT-3 after deployment (Machine Learning Paper Explained)
Views: 1
0
0
#nlp #gpt3 #prompt
Large language models such as GPT-3 have enabled many breakthroughs and new applications recently, but they come with an important downside: Training them is very expensive, and even fine-tuning is often difficult. This paper presents an adaptive method to improve performance of such models after deployment, without ever changing the model itself. This is done by maintaining a memory of interactions and then dynamically adapting new prompts by augmenting them with memory content. This has many applications, from non-intrusive fine-tuning to personalization.
Sponsor: Introduction to Graph Neural Networks Course
OUTLINE:
0:00 - Intro
0:40 - Sponsor: Introduction to GNNs Course (link in description)
1:30 - Paper Overview: Improve GPT-3 after deployment via user feedback
5:30 - Proposed memory-based architecture
13:00 - A detailed look at the components
15:00 - Example tasks
24:30 - My conce