Earlier quoted context omitted.
> The results are surprisingly good, I don't think I could've done better as a human I'm actually surprised that the performance is so poor and would expect a human to do much better. The GPT model has embedding PLUS a whole transformer model that can untangle the embedded structure. To clarify some of the issues: data is both singular and plural, being a mass noun[0,1]. Datum is something you'll find in the dictiona…
You are being unnecessarily cynical. These are all subjective. I thought "datum" and "datasets" was quite clever, and while I would've chosen "man" for "king - crown" myself, I actually find "ruler" a better solution after seeing it. But each to their own. The rant about network architecture misses my point, which is that an LLM does not just do a linear transformation and a similarity search. Sure, in the most abstr…
You're right that there's subjectivity but not infinitely so. There is a bound to this and that's both required for language to work and for us to build these models. I did agree that the data one was tricky so not really going to argue, I was just pointing out a critical detail given that the models learn through pattern matching rather than a dictionary. It's why I made the comment about humans. As for ruler minus crown, I gave my explication, would you care to share yours? I'd like to understand your point of view so I can better my interpretation of the results, because frankly I don't understand. What is the semantic relationship being changed if not the attribute of ruler?
The architecture part was a miscommunication. I hope you understand how I misunderstood you when you said "this doesn't do embedding math like OP!". It is clear I'm not alone either.
> Actually, to be VERY pedantic, that would not even be true, because ChatGPT's tokenizer embeds tokens, not words.
I was just trying to help you understand a different viewpoint.