11–20 of 159 posts
NLP is an important part of upcoming RAG frameworks like Microsoft’s LazyGraphRAG. So I think it’s more like NLP is a tool used when the time is right. https://www.microsoft.com/en-us/research/blog/lazygraphrag-s...
No, it has not and will not in the foreseeable future. This is one of my responsibilities at work. LLMs are not feasible when you have a dataset of 10 million items that you need to classify relatively fast and at a reasonable cost. LLMs are great at mid-level complexity tasks given a reasonable volume of data - they can take away the tedious job of figuring out what you are looking at or even come up with some basic…
Things are such an opportunity cost now days. It’s like trying to capture value out of a transient amorphous cloud, you can’t hold any of it in your hand but the phenomenon is clearly occurring.
No, it has not and will not in the foreseeable future. This is one of my responsibilities at work. LLMs are not feasible when you have a dataset of 10 million items that you need to classify relatively fast and at a reasonable cost. LLMs are great at mid-level complexity tasks given a reasonable volume of data - they can take away the tedious job of figuring out what you are looking at or even come up with some basic…
> One prompt? Fair. 10? Still ok. 100? You're pushing it. 10M - get help.
Assuming you could do 10M+ LLM calls for this task at trivial cost and time, would you do it? i.e. is the only thing keeping you away from LLM the fact they're currently too cumbersome to use?
I have worked on AWS Connect (online call center) and Amazon Lex (the backing NLP engine) projects.
Before LLMs, it was a tedious process of trying to figure out all of the different “utterances” that people could say and the various languages you had to support. With LLMs, it’s just prompting
https://chatgpt.com/share/678bab08-f3a0-8010-82e0-32cff9c0b4...
I used something like this using Amazon Bedrock and a Lambda hook for Amazon Lex. Of course it wasn’t booking a flight. It was another system
The above is a simplified version. In the real world , I gave it a list of intents (book flights, reserve a room, rent a car) and properties - “slots” - I needed for each intent.
No, it has not and will not in the foreseeable future. This is one of my responsibilities at work. LLMs are not feasible when you have a dataset of 10 million items that you need to classify relatively fast and at a reasonable cost. LLMs are great at mid-level complexity tasks given a reasonable volume of data - they can take away the tedious job of figuring out what you are looking at or even come up with some basic…
That’s the argument the article makes but the reasoning is a little questionable on a few fronts: - It uses f16 for the data format whereas quantization can reduce the memory burden without a meaningful drop in accuracy, especially as compared with traditional NLP techniques. - The quality of LLMs typically outperform OpenCV + NER. - You can choose to replace just part of the pipeline instead of using the LLM for eve…
That’s obviously not sustainable indefinitely, but these kinds of exponentials are precisely why people often make incorrect conclusions on how long change will take to happen. Just a reminder: CPUs were 2x more performance every 18 months and continued to continually upend software companies for 20 years who weren’t in tune with this cycle (i.e. focusing on performance instead of features). For example, even if you’re spending $10k/month for LLM vs $100/month to process the 10M item, it can still be more beneficial to go the LLM route as you can buy cheaper expertise to put together your LLM pipeline than the NLP route to make up the ~100k/year difference (assuming the performance otherwise works and the improved quality and robustness of the LLM solution isn’t providing extra revenue to offset).
For my use case, definitely. I have worked on AWS Connect (online call center) and Amazon Lex (the backing NLP engine) projects. Before LLMs, it was a tedious process of trying to figure out all of the different “utterances” that people could say and the various languages you had to support. With LLMs, it’s just prompting https://chatgpt.com/share/678bab08-f3a0-8010-82e0-32cff9c0b4... I used something like this using…
No, it has not and will not in the foreseeable future. This is one of my responsibilities at work. LLMs are not feasible when you have a dataset of 10 million items that you need to classify relatively fast and at a reasonable cost. LLMs are great at mid-level complexity tasks given a reasonable volume of data - they can take away the tedious job of figuring out what you are looking at or even come up with some basic…
Text classification, clustering, named entity recognition, etc. are NLP tasks. LLMs can perform these tasks. ML models that are not LLMs (or even not deep learning models) can also perform these tasks. Is the author perhaps asking if the concept of a "completion" has replaced all of these tasks?
When I hear "traditional NLP" I think not of the above types of tasks but rather the methodology employed for performing them. For example, building a pipeline to do stemming/lemmatization, part of speech tagging, coreference resolution, etc. before the text gets fed to a classifier model. This was SOTA 10 years ago but I don't think many people are still doing it today.
For my use case, definitely. I have worked on AWS Connect (online call center) and Amazon Lex (the backing NLP engine) projects. Before LLMs, it was a tedious process of trying to figure out all of the different “utterances” that people could say and the various languages you had to support. With LLMs, it’s just prompting https://chatgpt.com/share/678bab08-f3a0-8010-82e0-32cff9c0b4... I used something like this using…
How about the the costs?
No, it has not and will not in the foreseeable future. This is one of my responsibilities at work. LLMs are not feasible when you have a dataset of 10 million items that you need to classify relatively fast and at a reasonable cost. LLMs are great at mid-level complexity tasks given a reasonable volume of data - they can take away the tedious job of figuring out what you are looking at or even come up with some basic…
I imagine this wouldn't be the case if we were to do more classification projects, but we aren't. We did try to find replacements first, but it was impossible for us to attract any talent, which isn't too much of a surprise considering it's mainly maintenance. Using external consultants for that maintenance proved to be almost more expensive than having two full time employees.