I have gone over in my previous videos how to fine-tune these large language models, but that requires a large amount of data. It is often the case that we don't have a large amount of data to train with. In that case, as long as we have a handful of cases, we can try using Few Shot Learning to teach the model with few examples.
GPT 3 Paper: [ Ссылка ]
Discord: [ Ссылка ]
Time Stamps
00:00 - Intro And Announcement
00:30 - Zero Shot vs Few Shot vs Fine-tuning
03:43 - Few Shot Learning Demos
04:42 - Sentiment Analysis Demo
06:42 - QA Demo
09:00 - Code Generation Demo
12:19 - Takeaways
13:14 - Outro
