> Similarly, certain ML tasks are now easy to implement even for dumb dumb web devs like me
For example?
For example?
Now, if cost is little concern you can use zero shot prompting on an inefficient model. If cost is a concern, you can use GPT4 to create your golden dataset way faster and cheaper than human annotations, and then train your more efficient model.
Some example NLP tasks could be classifiers, sentiment, extracting data from documents. But I’d be curious which areas of NLP __weren’t__ disrupted by LLMs.
Essentially come up with a potent generic model using human feedback, label and annotation for LLM e.g GPT 4, then use it to generate golden dataset for other new models without human in the loop, very innovative indeed.
Would you be willing to expand a little and give a brief example please? It would be really helpful for me to understand this a little better!