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I guess use case is everything. There are numerous reasons, not least of which confidentiality, why chatgpt is a no go for me.

What I'd like to see more of is systematic comparison between chatgpt and classic models. I was hoping to see a bit of this in this article and was disappointed.

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I appreciate the feedback, and also agree that chatgpt is a no-go for many use cases.

We're working on putting together a better comparison specifically along the lines of accuracy between the LLMs (chatgpt, bard, falcon) and also traditional models. Hope that one hits the spot for you! Are their specific metrics you think might be interesting? We were primarily looking at f1/accuracy for this task, but also attempting to see what types of classes they work well in using semantic similarity.

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