Has LLM killed traditional NLP?
medium.com
Has LLM killed traditional NLP?
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Re: Has LLM killed traditional NLP?
#2[deleted]
Re: Has LLM killed traditional NLP?
#3This article seems to be paywalled unfortunately. While LLMs are very useful when the tasks are complex and/or there is not a lot of training data, I still think traditional NLP pipelines have a very important role to play, including when:
- Depending on the complexity of the task and the required results, SVMs or BERT can be enough in many cases and take much lower resources, especially if there is a lot of training data available. Training these models with LLM outputs could also be an interesting approach to achieve this.
- When resources are constrained or latency is important.
- In some cases, there may be labeled data in certain classes that have no semantic connection between them, e.g. explaining the class to LLMs could be tricky.
Re: Has LLM killed traditional NLP?
#4NLP 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...
Re: Has LLM killed traditional NLP?
#5Re: Has LLM killed traditional NLP?
#6No, 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 mapping. But anything at large volumes.. Na. Real life example: "is '20 bottles of ferric chloride' a service or a product?"
One prompt? Fair. 10? Still ok. 100? You're pushing it. 10M - get help.
Re: Has LLM killed traditional NLP?
#7This article seems to be paywalled unfortunately. While LLMs are very useful when the tasks are complex and/or there is not a lot of training data, I still think traditional NLP pipelines have a very important role to play, including when: - Depending on the complexity of the task and the required results, SVMs or BERT can be enough in many cases and take much lower resources, especially if there is a lot of training…
> This article seems to be paywalled unfortunately.
I am no fan of Medium paywalled articles but if it helps you, here's the article on archive - https://archive.is/J53CE
Re: Has LLM killed traditional NLP?
#8No, 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…
So TLDR: You agree with the author, but not for the same reasons?
Re: Has LLM killed traditional NLP?
#9No, 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 everything (e.g. using text-only 3B or 1B models to replace the NER model while keeping OpenCV)
- The (LLM compute / quality) / watt is constantly decreasing. Meaning even if it’s too expensive today, the system you’ve spent time building, tuning and maintaining today is quickly becoming obsolete.
- Talking with new grads in NLP programs, all the focus is basically on LLMs.
- The capability + quality out of models / size of model keeps increasing. That means your existing RAM & performance budget keeps absorbing problems that seemed previously out of reach
Now of course traditional techniques are valuable because they can be an important tool in bringing down costs (fixed function accelerator vs general purpose compute), but it’s going to become more niche and specialized with most tasks transitioning to LLMs I think.
The “bitter lesson” paper is really relevant to these kinds of discussions.
Re: Has LLM killed traditional NLP?
#10No, 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…
Prompt caching would lower the cost, later similar tech would lower the inference cost too.
You have less than 25 tokens, thats between 1-5$.
There may be some use case but I'm not convinced with the one you gave.