GPT4: Zero-shot Classification without any examples + Fine-tune with reflection

Channel:
Singapore John Tan Chong Min
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Published on ● Video Link: https://www.youtube.com/watch?v=C0Eug9XpcBo


Duration: 6:17
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Recently, I have played around with GPT4 and found out that it can classify data without any human-labelled examples, if the classification boundaries can be described in words that are commonly occuring throughout the web. This makes for very cool applications of annotating data without any human-annotated examples.

Also, the model is capable of refining its output using Reflexion-based techniques of reflecting on the answer and outputting the refined answer. It is able to even come up with a new category to fit the data.

This is impressive, and I am sure there will be more features of the GPT series which will be discovered as more people play around with it.

Explore and have fun!

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References:

Reflexion: https://arxiv.org/abs/2303.11366
ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks: https://arxiv.org/abs/2303.15056
Auto-GPT: https://github.com/Torantulino/Auto-GPT
~~~~~

0:00 Zero-shot Classification (or you can call it few-shot since we describe the categories)
3:24 Fine-tuning with Reflexion

AI and ML enthusiast. Likes to think about the essences behind breakthroughs of AI and explain it in a simple and relatable way. Also, I am an avid game creator.

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