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 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.
You are not pushing it at 100. I can classify "Is 20 bottles of ferric chloride' a service or product in probably 2 seconds with a 4090. Something that most people don't realize is you can run multiple inference. So with something like a 4090, some solid few shots, and instead of having it classify one example at a time, you can do 5. We can probably run 100 parallel inference at 5 at a time. For about a rate of 250 a second on a 4090. So in 11 hours I'll be done. I'm going with a 7-8B model too. Some of the 1.5-3B models are great and will even run faster. Take a competent developer who knows python and how to use an OpenAI compatible API, they can put this together in 10-15 minutes, with no data science/scikit learn or other NLP toolchain experience.
So for personal, medium or even large workloads, I think it has killed it. It needs to be extremely large. If you are classifying or segmenting comments on a social media platform were you need to deal with billions a day, then LLM would be a very inefficient approach, but for 90+% of use cases. I think it wins.
I'm assuming you are going to run it locally because everyone is paranoid about their data. It's even cheaper if you use a cloud API.
If you have to classify user input as they’re inputting it to provide a response — so it can’t be batched - 2 seconds could potentially be really slow.
Though LLMs sure have made creating training data to train old school models for those cases a lot easier.
Yes and no. Having used these tools extensively I think it will be some time before LLMs are truly performant. Even smaller models can't be compared to running optimized code with efficient data structures. And smaller models (in general) do reduce the quality of your results in most cases. Maybe LLMs will kill off NLP and other pursuits pretty soon, but at the moment, each have their tradeoffs.
Correct me if I'm wrong, but, if you run multiple inferences at the same time on the same GPU you will need load multiple models in the vram and the models will fight for resources right? So running 10 parallel inferences will slow everything down 5 times right? Or am I missing something?
even more naive way - just club several requests into batch of classification requests into one prompt. in practice, this is not production-ready as the llm output does not always contain results for the same number of input (sometimes more than inputted even!)
FFS... "Lots of writers, few readers". Read again and do the math: 2 seconds, multiply that by 10 million records which contain this, as well as "alarm installation in two locations" and a whole bunch of other crap with little to no repetition (<2%) and where does that get you? 2 * 10,000,000 = 20,000,000 SECONDS!!!! A day has 86,400 seconds (24 * 3600 = 86,400). The data pipeline needs to finish in <24 hours. Everyone needs to get this into their heads somehow: LLM's are not a silver bullet. They will not cure cancer anytime soon, nor will they be effective or cheap enough to run at massive scale. And I don't mean cheap as in "oh, just get openai subscription hurr durr". Throwing money mindlessly into something is never an effective way to solve a problem.
You might be able to use an even cheaper model. Google Gemini 1.5 Flash 8B is Input: $0.04 / Output: $0.15 per 1M tokens.
17 input tokens and 2 output tokens * 10 million jobs = 170,000,000 input tokens, 20,000,000 output tokens... which costs a total of $6.38 https://tools.simonwillison.net/llm-prices
As for rate limits, https://ai.google.dev/pricing#1_5flash-8B says 4,000 requests per minute and 4 million tokens per minute - so you could run those 10 million jobs in about 2500 minutes or 42 hours. I imagine you could pull a trick like sending 10 items in a single prompt to help speed that up, but you'd have to test carefully to check the accuracy effects of doing that.
The question is not average cost but marginal cost of quality - same as voice recognition, which had relatively low uptake even at ~2-4% error rates due to context switching costs for error correction.
So you'd have to account for the work of catching the residue of 2-8%+ error from LLMs. I believe the premise is for NLP, that's just incremental work, but for LLM's that could be impossible to correct (i.e., cost per next-percentage-correction explodes), for lack of easily controllable (or even understandable) models.
But it's most rational in business to focus on the easy majority with lower costs, and ignore hard parts that don't lead to dramatically larger TAM.
This assumes you don’t care about our rapidly depleting carbon budget.
No matter how much energy you save personally, running your jobs on Sam A’s earth killer ten thousand cluster of GPUs is literally against your own self interest of delaying climate disasters.
LLM have huge negative externalities, there is a moral argument to only use them when other tools won’t work.
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.
Not an independent player so obviously important to be critical of papers like this [1], but it’s claiming a ~10x cost in LLM inference every year. This lines up with the technical papers I’m seeing that are continually improving performance + the related HW improvements.
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).
That’s sort of like asking a horse and buggy driver whether automobiles are going to put them out of business.
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.
How about a naive Bayesian Bag of Words? Just find/scrape/generate with an LLM a large enough corpus of products/services, build the term frequency matrix, calculate class priors and P(term|class) and inference with straightforward application of Bayes theorem.
This particular problem, at least to me, seems trivial, and to use an LLM for anything like this for more than a hundred cases seems incredibly wasteful.
While I agree with both you and the article I also think it'll depend on more than just the volume of your data. We have quite a lot of documents that we classify. It's around 10-100k a month, some rather large others simple invoices. We used to have a couple of AI specialists who handled the classification with local NLP models, but when they left we had to find alternatives. For us this was the AI services in the cloud we use and the result has been a document warehouse which is both easier for the business to manage and a "pipeline" which is much cheaper than having those AI specialists on the payroll.
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.
I suspect any solution like that will be wholesale thrown away in a year or two. Unless the damn thing is going to make money in the next 2-3 years, we are all mostly going to write throwaway code.
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.
Can you talk about the main non-LLM NLP tools you use? e.g. BERT models?
> 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 see LLMs best used as part of a more traditional NLP pipeline.
For example, an approach that does me well is clustering then using LLMs on representative docs. Tools like bertopic are great for this.
I also don't see a clear cut difference between the two in certain areas. Embeddings are critical in LLM pipelines, but for me anyway, also "old school" tools.
I think NLP as described in the article is certainly under threat, but the tools and approaches compliment LLM use well, are far more efficient, and distinguish the pros from the neophytes.
If you're using LLMs for NLP-type tasks, but don't know the NLP tools, you're missing out.
You can use embeddings to build classification models using various methods. Not sure what qualifies as "get help" level of cost/throughput, but certainly most providers offer large embedding APIs at much lower cost/higher throughput than their completion APIs.
Say Gemini Flash 8B, allowing ~28 tokens for prompt input at $0.075/1M tokens, plus 2 output tokens at $0.30/1M. Works out to $0.0027 per classification. Or in other words, for 1 penny you could do this classification 3.7 times.
So there's a bit of an issue with prompt caching implementations: for both OpenAI API and Claude's API, you need a minimum of 1024 tokens to build the cache for whatever reason. For simple problems, that can be hard to hit and may require padding the system prompt a bit.
> 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.
What? That's simply not true.
Current embedding models are incredibly fast and cheap and will, in the vast majority of NLP tasks, get you far better results than any local set of features you can develop yourself.
I've also done this at work numerous times, and have been working on various NLP tasks for over a decade now. For all future traditional NLP tasks the first pass is going to be to get fetch LLM embeddings and stick on a fairly simple classification model.
> One prompt? Fair. 10? Still ok. 100? You're pushing it. 10M - get help.
"Prompting" is not how you use LLMs for classification tasks. Sure you can build 0-shot classifiers for some tricky tasks, but if you're doing classification for documents today and you're not starting with an embedding model you're missing some easy gains.
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
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.
Thank you for sharing an actual prompt thread. So much of the LLM debate is washed in biases, and it is very helpful to share concrete examples of outputs.
The “cordele GA” example surprised me. I was expecting to get a value of “null” for the airport code since I knew that city had a population of 12K and no airport within its metropolitan statistical area. It returned an airport that was close.
Having world knowledge is a godsend. I also just tried a prompt with “Alpharetta, GA” a city north of Atlanta and it returned ATL. An NLP could never do that without a lot more work.
That’s a great example and I understand it was intentionally simple but highlighted how LLMs need care with use. Not that this example is very related to NLP:
My prompt:
`<<I want a flight from portland to cuba after easter>>`
Of course I meant Portland Maine (PWM), there is more than one airport option in Cuba than HAV, and it got the date wrong, since Easter is April 20 this year.
If the business stakeholders came out with that scenario, I would modify the prompt like this. You would know the users address if they had an account.
We measure savings in terms of call deflections. Clients we work with say that each time a customer talks to an agent it costs $2-$5. That’s not even taking into account call abandonments
you are a chatbot that helps users book flights. Please extract the origin city, destination city, travel date, and any additional preferences (e.g., time of day, class of service). If any of the details are missing, make the value “null”. If the date is relative (e.g., "tomorrow", "next week"), convert it to a specific date.
User Input: "<User's Query>"
Output (JSON format):
{
"origin": list of airport codes
"destination": list of airport codes,
"date": "<Extracted Date>",
"departure_time": "<Extracted Departure Time (if applicable)>",
"preferences": "<Any additional preferences like class of service (optional)>"
}
The users request will be surrounded by <<>>
Always return JSON with any missing properties having a value of null. Always return English. Return a list of airport codes for the city. For instance New York has two airports give both
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.
I understand the term the same way, but I don't think "traditional NLP" died with LLMs, traditional NLP already died with deep learning 7ish years ago, with LSTMs and CNNs, the gains from such models trained end-to-end are so huge it just doesn't make sense to chop up the tasks like that.
I created an NLP library to help curl straight quotes into curly quotes. Last I checked, LLMs struggled to curl the following straight quotation marks:
''E's got a 'ittle box 'n a big 'un,' she said, 'wit' th' 'ittle 'un 'bout 2'×6". An' no, y'ain't cryin' on th' "soap box" to me no mo, y'hear. 'Cause it 'tweren't ever a spec o' fun!' I says to my frien'.
The library is integrated into my Markdown editor, KeenWrite (https://keenwrite.com/), to correctly curl quotation marks into entities before passing them over to ConTeXt for typesetting. While there are other ways to indicate opening and closing quotation marks, none are as natural to type in plain text as straight quotes. I would not trust an LLM curl quotation marks accurately.
If you find any edge cases that don't work, do let me know. The library correctly curls my entire novel. There are a few edge cases that are completely ambiguous, however, that require semantic knowledge (part-of-speech tagging), which I haven't added. PoS tagging would be a heavy operation that could prevent real-time quote curling for little practical gain.
The lexer, parser, and test cases are all open source.
Great example. I just tried it with a few LLMs and got horrible results. GPT-4o got a ton of them wrong, GPT-1o got them all correct AFAICT but took 1m50s to do so, and Claude 3.5 Sonnet said “Here's the text with straight quotes converted to curly quotes” but then returned the text with all the straight quotes intact.
I’m very surprised all three models didn’t nail it immediately.
I would be interested how well would even a smaller LLM model work after fine tuning. Besides the overhead of an LLM, I would assume they would do a much better job at it in the edge cases (where contextual understanding is required).
I remember using the open NLP library from Stanford around 2016. It would do parts of speech tagging of words in a sentence (labelling the words with their grammatical function). It was pretty good but reliably failed on certain words where context determined the tag. When for gpt 3 came out the first thing I tested it out on was parts of speech tagging. In particular those sentences open NLP had trouble with. And it aced everything I was impressed.
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.
I could use some help understanding, is this a set of tools or techniques to answer questions? The name made me think it's related to create embeddings but it seems much more?
One of the things I'm still struggling with when using LLMs over NLP is classification against a large corpus of data. If I get a new text and I want to find the most similar text out of a million others, semantically speaking, how would I do this with an LLM? Apart from choosing certain pre-defined categories (such as "friendly", "political", ...) and then letting the LLM rate each text on each category, I can't see a simple solution yet except using embeddings (which I think could just be done using BERT and does not count as LLM usage?).
I've used embeddings to define clusters, then passed sampled documents from each cluster to an LLM to create labels for each grouping. I had pretty impressive results from this approach when creating a category/subcategory labels for a collection of texts I worked on recently.
That's interesting, it sounds a bit like those cluster graph visualisation techniques. Unfortunately, my texts seem to fall into clusters that really don't match the ones that I had hoped to get out of these methods. I guess it's just a matter of fine-tuning now.
Feed one through an LLM, one word at a time, and keep track of words that experience greatly inflated probabilities of occurrence, compared to baseline English. "For" is probably going to maintain a level of likelihood close to baseline. "Engine" is not.
Wouldn't a simple comparison of the word frequency in my text against a list of usual word frequencies do the trick here without an LLM? Sort of a BM25?
Readit News
One prompt? Fair. 10? Still ok. 100? You're pushing it. 10M - get help.
So for personal, medium or even large workloads, I think it has killed it. It needs to be extremely large. If you are classifying or segmenting comments on a social media platform were you need to deal with billions a day, then LLM would be a very inefficient approach, but for 90+% of use cases. I think it wins.
I'm assuming you are going to run it locally because everyone is paranoid about their data. It's even cheaper if you use a cloud API.
Though LLMs sure have made creating training data to train old school models for those cases a lot easier.
System message: answer with just "service" or "product"
User message (variable): 20 bottles of ferric chloride
Response: product
Model: OpenAI GPT-4o-mini
$0.075/1Mt batch input * 27 input tokens * 10M jobs = $20.25
$0.300/1Mt batch output * 1 output token * 10M jobs = $3.00
It's a sub-$25 job.
You'd need to be doing 20 times that volume every single day to even start to justify hiring an NLP engineer instead.
17 input tokens and 2 output tokens * 10 million jobs = 170,000,000 input tokens, 20,000,000 output tokens... which costs a total of $6.38 https://tools.simonwillison.net/llm-prices
As for rate limits, https://ai.google.dev/pricing#1_5flash-8B says 4,000 requests per minute and 4 million tokens per minute - so you could run those 10 million jobs in about 2500 minutes or 42 hours. I imagine you could pull a trick like sending 10 items in a single prompt to help speed that up, but you'd have to test carefully to check the accuracy effects of doing that.
So you'd have to account for the work of catching the residue of 2-8%+ error from LLMs. I believe the premise is for NLP, that's just incremental work, but for LLM's that could be impossible to correct (i.e., cost per next-percentage-correction explodes), for lack of easily controllable (or even understandable) models.
But it's most rational in business to focus on the easy majority with lower costs, and ignore hard parts that don't lead to dramatically larger TAM.
No matter how much energy you save personally, running your jobs on Sam A’s earth killer ten thousand cluster of GPUs is literally against your own self interest of delaying climate disasters.
LLM have huge negative externalities, there is a moral argument to only use them when other tools won’t work.
How much for the “prompt engineer”? Who is going to be doing the work and validating the output?
- 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.
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).
[1] https://a16z.com/llmflation-llm-inference-cost/
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.
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.
No, you can't. The only thing LLM's replace is internet commentators.
this is how you end up with 1000s of lines of slop that you have no idea how it functions.
This particular problem, at least to me, seems trivial, and to use an LLM for anything like this for more than a hundred cases seems incredibly wasteful.
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.
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.
> 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 would believe that many NLP problems can be easily solved even by smaller LLM models.
Deleted Comment
For example, an approach that does me well is clustering then using LLMs on representative docs. Tools like bertopic are great for this.
I also don't see a clear cut difference between the two in certain areas. Embeddings are critical in LLM pipelines, but for me anyway, also "old school" tools.
I think NLP as described in the article is certainly under threat, but the tools and approaches compliment LLM use well, are far more efficient, and distinguish the pros from the neophytes.
If you're using LLMs for NLP-type tasks, but don't know the NLP tools, you're missing out.
This is a real problem I am dealing with at a library project.
Each document is between 100 to 10k tokens.
Most top (read most expensive) LLMs available in OpenRouter work great, it is the cost (and speed) that is the issue.
If I could come up with something locally runnable that would be fantastic.
Presumably BERT based classifiers would work if I had one properly trained for the language.
Claude Haiku is $0.25 per 1M tokens in, $1.05 per 1M out, so cost would be ~$35.
GPT-4o mini is even cheaper at $0.15 per 1M in.
Of course if your volume justifies the hardware cost you could always run Llama locally, for the cost of the electricity used.
Say Gemini Flash 8B, allowing ~28 tokens for prompt input at $0.075/1M tokens, plus 2 output tokens at $0.30/1M. Works out to $0.0027 per classification. Or in other words, for 1 penny you could do this classification 3.7 times.
There may be some use case but I'm not convinced with the one you gave.
What? That's simply not true.
Current embedding models are incredibly fast and cheap and will, in the vast majority of NLP tasks, get you far better results than any local set of features you can develop yourself.
I've also done this at work numerous times, and have been working on various NLP tasks for over a decade now. For all future traditional NLP tasks the first pass is going to be to get fetch LLM embeddings and stick on a fairly simple classification model.
> One prompt? Fair. 10? Still ok. 100? You're pushing it. 10M - get help.
"Prompting" is not how you use LLMs for classification tasks. Sure you can build 0-shot classifiers for some tricky tasks, but if you're doing classification for documents today and you're not starting with an embedding model you're missing some easy gains.
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.
Having world knowledge is a godsend. I also just tried a prompt with “Alpharetta, GA” a city north of Atlanta and it returned ATL. An NLP could never do that without a lot more work.
My prompt: `<<I want a flight from portland to cuba after easter>>`
The response: ``` { "origin": ["PDX"], "destination": ["HAV"], "date": "2025-04-01", "departure_time": null, "preferences": null } ```
Of course I meant Portland Maine (PWM), there is more than one airport option in Cuba than HAV, and it got the date wrong, since Easter is April 20 this year.
https://chatgpt.com/share/678c1708-639c-8010-a6be-9ce1055703...
The prompt:
you are a chatbot that helps users book flights. Please extract the origin city, destination city, travel date, and any additional preferences (e.g., time of day, class of service). If any of the details are missing, make the value “null”. If the date is relative (e.g., "tomorrow", "next week"), convert it to a specific date.
User Input: "<User's Query>"
Output (JSON format): { "origin": list of airport codes "destination": list of airport codes, "date": "<Extracted Date>", "departure_time": "<Extracted Departure Time (if applicable)>", "preferences": "<Any additional preferences like class of service (optional)>" }
The users request will be surrounded by <<>>
Always return JSON with any missing properties having a value of null. Always return English. Return a list of airport codes for the city. For instance New York has two airports give both
Always return responses in English
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 the curious, you can try it at:
https://whitemagicsoftware.com/keenquotes/
If you find any edge cases that don't work, do let me know. The library correctly curls my entire novel. There are a few edge cases that are completely ambiguous, however, that require semantic knowledge (part-of-speech tagging), which I haven't added. PoS tagging would be a heavy operation that could prevent real-time quote curling for little practical gain.
The lexer, parser, and test cases are all open source.
https://gitlab.com/DaveJarvis/KeenQuotes/-/tree/main/src/mai...
I’m very surprised all three models didn’t nail it immediately.
https://www.microsoft.com/en-us/research/blog/lazygraphrag-s...
Feed one through an LLM, one word at a time, and keep track of words that experience greatly inflated probabilities of occurrence, compared to baseline English. "For" is probably going to maintain a level of likelihood close to baseline. "Engine" is not.
Do the same thing for the other one.
See how much overlap you get.