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Earlier quoted context omitted.
I have always known N/A as not available.
Curious, where are you from? If I Google N/A every single hit on the first page is explaining it means "Not applicable" are you from a non-english country? Maybe its cultural?
> N/A (or sometimes n/a or N.A.) is a common abbreviation in tables and lists for the phrase not applicable, not available, not assessed, or no answer.
A bit off-topic but maybe not? Any words on GPT-5? Is that coming? Or is OpenAI just focusing on the Sora model?
They would likely wait till any model performs better than GPT 4 for the same price
There are so many applications for LLMs where having a perfect score is much more important than speed, because getting it wrong is so expensive, damaging, or time consuming to resolve for an organisation.
Earlier quoted context omitted.
Curious, where are you from? If I Google N/A every single hit on the first page is explaining it means "Not applicable" are you from a non-english country? Maybe its cultural?
The first entry on Google is Wikipedia [1] for me: > N/A (or sometimes n/a or N.A.) is a common abbreviation in tables and lists for the phrase not applicable, not available, not assessed, or no answer. [1] https://en.wikipedia.org/wiki/N/A
Meaning of n/a in English written abbreviation for not applicable: used on a form to show that you are not giving the information asked for because the question is not intended for you or your situation: If a question does not apply to you, please put N/A in the box provided. COMMERCE.
TIL
A bit off-topic but maybe not? Any words on GPT-5? Is that coming? Or is OpenAI just focusing on the Sora model?
There are so many applications for LLMs where having a perfect score is much more important than speed, because getting it wrong is so expensive, damaging, or time consuming to resolve for an organisation.
The problem I'm finding is that the time I wanted to save mantaining selectors and the like is time that I'm spending writing wrapper code and dealing with the mistakes it makes. Some are OK and can deal with them, others are pretty annoying because It's difficult to deal with them in a deterministic manner.
I've also tried with GPT-4 but it's way more expensive, and despite what this guy got, it also makes mistakes.
I don't really care about inference speed, but I do care about price and correctness.
Finally, something more offensive than parsing HTML with regular expressions: parsing HTML with LLMs.
I for one am glad I can offload all the regex to LLMs. Powerful? Yes. Human readable for beginners? No.
There are so many applications for LLMs where having a perfect score is much more important than speed, because getting it wrong is so expensive, damaging, or time consuming to resolve for an organisation.
Sorry to be nit-picky but thats the essence of these benchmarks - Mistral putting "N/A" for not available is weird - N/A is not applicable, in every use I have ever seen, and they DONT mean the same thing. I would expect null for not available and N/A for not applicable Impressive inference speed difference though