Inference costs, not training costs.
> The fact that you can replace programmers
You can’t… not for any real project. For quick mockups they’re serviceable
> That’s sort of like asking a horse and buggy driver whether automobiles
Kind of an insult to OP, no? Horse and buggy drivers were not highly educated experts in their field.
Maybe take the word of domain experts rather than AI company marketing teams.
Why does training cost matter if you have a general intelligence that can do the task for you, that’s getting cheaper to run the task on?
> for quick mockups they’re serviceable
I know multiple startups that use LLMs as their core bread-and-butter intelligence platform instead of tuned but traditional NLP models
> take the word of domain experts
I guess? I wouldn’t call myself an expert by any means but I’ve been working on NLP problems for about 5 years. Most people I know in NLP-adjacent fields have converged around LLMs being good for most (but obviously not all) problems.
> kind of an insult
Depends on whether you think OP intended to offend, ig
Assuming we didn’t need to train it ever again, it wouldn’t. But we don’t have that, so…
> I know multiple startups that use LLMs as their core bread-and-butter intelligence platform instead of tuned but traditional NLP models
Okay? Did that system write itself entirely? Did it replace the programmers that actually made it?
If so, they should pivot into a Devin competitor.
> Most people I know in NLP-adjacent fields have converged around LLMs being good for most (but obviously not all) problems.
Yeah LLMs are quite good at comming NLP tasks, but AFAIK are not SOTA at any specific task.
Either way, LLMs obviously don’t kill the need for the NLP field.
It seems like LLMs would be perfect for start-ups that are iterating quickly. As the business, problem, and data mature though I would expect those LLMs to be consolidated into simpler models. This makes sense from a cost and reliability perspective. I wonder also about the impact of making your core IP a set of prompts beholden to the behavior of someone else’s model.
Appeal to authority is a well known logical fallacy.
I know how dead NLP is personally because I’ve never been able to get NLP working but once ChatGPT came around, I was able to classify texts extremely easily. It’s transformational.
I was able to get ChatGPT to classify posts based on how political it was from a scale of 1 to 10 and which political leaning they were and then classify the persons likely political affiliations.
All of this without needing to learn any APIs or anything about NLPs. Sorry but given my experience, NLPs are dead in the water right now, except in terms of cost. And cost will go down exponentially as they always do. Right now I’m waiting for the RTC 5090 so I can just do it myself with open source LLM.
While LLM’s can have their uses, let’s not get carried away.
Context for the problem space:
It does seem likely we’ll soon have cheap enough LLM inference to displace traditional NLP entirely, although not quite yet.
I did not make an appeal to authority. I made an appeal to expertise.
It’s why you’d trust a doctor’s medical opinion over a child’s.
I’m not saying “listen to this guy because their captain of NLP” I’m saying listen because experts have spent years of hands on experience with things like getting NLP working at all.
> I know how dead NLP is personally because I’ve never been able to get NLP working
So you’re not an expert in the field. Barely know anything about it, but you’re okay hand waving away expertise bc you got a toy NLP Demo working…
That’s great, dude.
> I was able to get ChatGPT to classify posts based on how political it was from a scale of 1 to 10
And I know you didn’t compare the results against classic NLP to see if there was any improvements because you don’t know how…
Lol
> I’m saying listen because experts have spent years of hands on experience with things like getting NLP working at all.
“It is difficult to get a man to understand something, when his salary depends on his not understanding it.”
Upton Sinclair
> Barely know anything about it, but you’re okay hand waving away expertise bc you got a toy NLP Demo working…
Yes that’s my point. I don’t know anything about implementing an NLP but got something that works pretty well using an LLM extremely quickly and easily.
> And I know you didn’t compare the results against classic NLP to see if there was any improvements because you don’t know NLP…
Do you cross reference all your Google searches to make sure they are giving you the best results vs Bing and DDG?
Do you cross reference the results from your NLP with LLMs to see if there were any improvements?
Great argument
> “It is difficult to get a man to understand something, when his salary depends on his not understanding it.”
NLP professionals are also LLM professionals. LLMs are tools in an NLP toolkit. LLMs don’t make the NLP professional obsolete the way it makes handwritten spam obsolete.
I was going to explain this further but you literally wouldn’t understand.
> Do you cross reference all your Google searches to make sure they are giving you the best results vs Bing and DDG?
…Yes I do…
That’s why I cancelled my kagi subscription. It was just as good as DDG.
> Do you cross reference the results from your NLP with LLMs to see if there were any improvements?
Yes I do… because I want to use the best tool for the job. Not just the first one I was able to get working…
False.
With all due respect, the fact that you're referring to natural language parsing as "NLPs" makes me question whether you have any experience or modest knowledge around this topic, so it's rather bold of you to make such sweeping generalizations.
It works for your use case because you're just one person running it on your home computer with consumer hardware. Some of us have to run NLP related processing (POS taggers, keyword extraction, etc) in a professional environment at tremendous scale, and reaching for an LLM would absolutely kill our performance.
No, you can't. The only thing LLM's replace is internet commentators.
Nah, searching Stackoverflow and Github doesn't take "weeks".
That said, due to how utterly broken internet search is nowadays, using an LLM as a search engine proxy is viable.
If you're going for rough approximation, LLMs are great, and good enough. More care and conventional ML methods are appropriate as the stakes increase though.
this is how you end up with 1000s of lines of slop that you have no idea how it functions.
I think for the most part, casual nlp is dead because of LLMs. And LLM costs are going to plummet soon, so large scale nlp that you’re talking about is probably dead within 5 years or less. The fact that you can replace programmers with prompts is huge in my opinion so no one needs to learn an nlm API anymore, just stuff it into a prompt. Once costs to power LLMs decrease to meet the cost of programmers it’s game over.