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When ChatGPT broke the field of NLP: An oral history

278 points by mathgenius 1y ago

AndrewKemendo 1y ago
If Chomsky was writing papers in 2020 his paper would’ve been “language is all you need.” That is clearly not true and as the article points out wide scale very large forecasting models beat that hypothesis that you need an actual foundational structure for language in order to demonstrate intelligence when in fact is exactly the opposite. I’ve never been convinced by that hypothesis if for no other reason that we can demonstrate in the real world that intelligence is possible without linguistic structure. As we’re finding: solving the markov process iteratively is the foundation of intelligence out of that process emerges novel state transition processes - in some cases that’s novel communication methods that have structured mapping to state encoding inside the actor communications happen across species to various levels of fidelity but it is not the underlying mechanism of intelligence, it is an emerging behavior that allows for shared mental mapping and storage
- aidenn0 1y ago
  Some people will never be convinced that a machine demonstrates intelligence. This is because for a lot of people, intelligence exists a subjective experience that they have and the belief that others have it too is only inasmuch as others appear to be like the self.
  - dekhn 1y ago
    This is why I want the field to go straight to building indistinguishable agents- specifically, you should be able to video chat with an avatar that is impossible to tell from a human. Then we can ask "if this is indistinguishable from a human, how can you be sure that anybody is intelligent?" Personally I suspect we can make zombies that appear indistinguishable from humans (limited to video chat; making a robot that appears human to a doctor would be hard) but that don't have self-consciousness or any subjective experience.
    - bsder 1y ago
      "There is considerable overlap between the intelligence of the smartest bears and the dumbest tourists." LLMs are not artificial intelligence but artificial stupidity. LLMs will happily hallucinate. LLMs will happily tell you total lies with complete confidence. LLMs will give you grammatically perfect completely vapid content. etc. And yet that is still better than what most humans could do in the same situation. We haven't proved that machines can have intelligence, but instead we are happily proving that most people, most of the time just aren't very intelligent at all.
      - JumpCrisscross 1y ago
        > yet that is still better than what most humans could do in the same situation Yup. A depressing takeaway from LLMs is most humans don’t demonstrate a drive to be curious and understand, but instead, to sort of muddle through most (economic) tasks.
      - vladms 1y ago
        > LLMs will happily hallucinate. LLMs will happily tell you total lies with complete confidence. Perhaps we should avoid anthropomorphizing them too much. LLMs don't inhabit a "real world" where they can experiment and learn. Their training data is their universe, and it's likely filled with conflicting, peculiar, and untestable information. Yes, the output is sometimes "a lie" if we apply it to our world, but in "their world" stuff is might be just strangely different. And it's not like the real world has only "hard simple truths" - quantum mechanics comes to mind about how strange stuff can be.
    - ActorNightly 1y ago
      Thats not really intelligence though.
  - 6stringmerc 1y ago
    It is until proven otherwise because modern science still doesn’t have a consensus or standards or biological tests which can account for it. As in, highly “intelligent” people often lack “common sense” or fall prey to con artists. It’s pompous as shit to assert a black box mimicry constitutes intelligence. Wake me up when it can learn to play a guitar and write something as good as Bob Dylan and Tom Petty. Hint: we’ll both be dead before that happens.
    - aidenn0 1y ago
      I can't write something as good as Bob Dylan and Tom Petty. Ergo I'm not intelligent.
      - coffeeaddict1 1y ago
        This to me is a weak argument. You have the ability to appreciate and judge something as good as Bob Dylan and Tom Petty. That's what makes you intelligent.
        motorest 1y ago
        > This to me is a weak argument. You have the ability to appreciate and judge something as good as Bob Dylan and Tom Petty. That's what makes you intelligent. What if you don't? Do you think that makes someone not intelligent? Think about it for a second.
        coffeeaddict1 1y ago
        Yes. If you do not possess the potential ability to judge other human beings and/or their work, you lack intelligence.
        aidenn0 1y ago
        1. I'm sure if I were to ask an LLM for opinions on Dylan and Petty, it would provide them. 2. I don't know if this was the point the original was making, but I personally think Dylan is a bit overrated as a songwriter (and the one time I saw him live, he was only so-so as a performer, but I don't think that's exactly a hot take).
  - meroes 1y ago
    It doesn’t mean they tie intelligence to subjective experience. Take digestion. Can a computer simulate digestion, yes. But no computer can “digest” if it’s just silicon in the corner of an office. There are two hurdles. The leap from simulating intelligence to intelligence, and the leap from intelligence to subjective experience. If the computer gets attached to a mechanism that physically breaks down organic material, that’s the first leap. If the computer gains a first person experience of that process, that’s the second. You can’t just short-circuit from simulates to does to has subjective experience. And the claim other humans don’t have subjective experience is such non-starter.
    - aidenn0 1y ago
      I think you're talking about consciousness rather than intelligence. While I do see people regularly distinguishing between simulation and reality for consciousness, I don't often see people make that distinction for intelligence. > And the claim other humans don’t have subjective experience is such non-starter. What about other primates? Other mammals? The smarter species of cephalopods? Certain many psychopaths seem to act as if they have this belief.
    - lupusreal 1y ago
      > And the claim other humans don’t have subjective experience is such non-starter. There is no empirical test for the subjective experience of consciousness. You can't even prove to anybody else that you have it. We assume other people experience as we ourselves do as a basic decency we extend to other humans. This is a good thing, but it's essentially faith not science. As for machines not having it, I'm fine with that assumption, but until there can be some sort of empirical test for it, it's not science. Thankfully, it's also not relevant to any engineering matter. Whether the machines have a subjective experience in any way comparable to our own doesn't touch any question about what demonstrable capabilities or limitations they have. We don't need to know if the computer has a ""soul"" to know if the computer can be a solution to any given engineering problem. Whether machines can have subjective experience shouldn't be considered an important question to engineers; let theologians waste their time fruitlessly debating that.
  - shmel 1y ago
    How do they convince themselves that other people have intelligence too?
  - simonw 1y ago
    It's called the AI effect: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect > The author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'."
    - qzw 1y ago
      > somebody figured out how to make a computer do something Well, I would argue that in most deterministic AI systems the thinking was all done by the AI researchers and then encoded for the computer. That’s why historically it’s been easy to say, “No, the machine isn’t doing any thinking, but only applying thinking that’s embedded within.” I think that line of argument becomes less obvious when you have learning systems where the behavior is training dependent. It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.
      - svieira 1y ago
        > It’s still fairly safe to argue that the best LLMs today are not ... thinking I agree completely. > But in another generation or two? It will become much harder to deny. Unless there is something ... categorically different about what an LLM does and in a generation or two we can articulate what that is (30 years of looking at something makes it easier to understand ... sometimes).
        aorloff 1y ago
        Intelligence requires agency
      - otabdeveloper4 1y ago
        In many ways LLMs are a regression compared to what was before. They solve a huge class of problems quickly and cheaply, but they also have severe limitations that older methods didn't have. So no, it's not a linear progress story like in a sci-fi story.
  - AndrewKemendo 1y ago
    Humans are basically incapable of recognizing that there’s something that’s more powerful than them They’re never going to actively collectively admit that that’s the case, because humans collectively are so so systematically arrogant and self possessed that they’re not even open to the possibility of being lower on the intelligence totem pole The only possible way forward for AI is to create the thing that everybody is so scared of so they can actually realize their place in the universe
    - skissane 1y ago
      > Humans are basically incapable of recognizing that there’s something that’s more powerful than them > They’re never going to actively collectively admit that that’s the case, because humans collectively are so so systematically arrogant and self possessed that they’re not even open to the possibility of being lower on the intelligence totem pole For most of human history, the clear majority of humans have believed in God(s), spirits, angels, bodhisattvas, etc - beings which by definition are above us on the “totem pole” - and although atheism is much more widespread today, I think it almost certainly remains a minority viewpoint at the global level. So I’m sceptical of your idea humans have some inherent unwillingness to believe in superhuman entities. From an atheist perspective, one might say that the (globally/historically) average human is so eager to believe in such entities, that if they don’t actually exist, they’ll imagine them and then convince themselves that their imaginings are entirely real. (Whereas, a theist might argue that the human eagerness to believe in such entities is better explained by their existence.)
      - AndrewKemendo 1y ago
        Religious people - depending on type - use the God as cosplay or as some kind of deus ex machina that they are merged with in their mind. Ask a deeply religious person about the separation between themselves and God and each tradition has their own version of being one with God or that they’ll become God etc… It’s all the same because it’s ultimately about their relationship with how they define their God - having a “religious experience” where you realize “you are a part of god and get benefit etc..” is frankly a requirement to be a religion In part that’s why it’s hard to call some versions of Buddhism religions and even harder for eg Hindus
  - AIPedant 1y ago
    I will not be convinced a machine demonstrates intelligence until someone demonstrates a robot that can navigate 3D space as intelligently as, say, a cockroach. AFAICT we are still many years away from this, probably decades. A bunch of human language knowledge and brittle heuristics doesn't convince me at all. This ad hominem is really irritating. People have complained since Alan Turing that AI research ignores simpler intelligence, instead trying to bedazzle people with fancy tricks that convey the illusion of human intelligence. Still true today: lots of talk about o3's (exaggerated) ability to do fancy math, little talk about its appallingly bad general quantitative reasoning. The idea of "jagged AI" is unscientific horseshit designed to sweep this stuff under the rug.
  - felipeerias 1y ago
    In the natural world, intelligence requires embodiment. And, depending on your point of view, consciousness. Modern AI exhibits neither of those characteristics.
- emp17344 1y ago
  > As we’re finding: solving the markov process iteratively is the foundation of intelligence No, the markov process allows LLMs to make connections between existing representations of human intelligence. LLMs are nothing without a data set developed by an existing intelligence.
criddell 1y ago
The field is natural language processing.
- dang 1y ago
  I think we can squeeze it in there. Thanks!
languagehacker 1y ago
Great seeing Ray Mooney (who I took a graduate class with) and Emily Bender (a colleague of many at the UT Linguistics Dept., and a regular visitor) sharing their honest reservations with AI and LLMs. I try to stay as far away from this stuff as possible because when the bottom falls out, it's going to have devastating effects for everyone involved. As a former computational linguist and someone who built similar tools at reasonable scale for largeish social media organizations in the teens, I learned the hard way not to trust the efficacy of these models or their ability to get the sort of reliability that a naive user would expect from them in practical application.
- philomath_mn 1y ago
  Curious what you are expecting when you say "bottom falls out". Are you expecting significant failures of large-scale systems? Or more a point where people recognize some flaw that you see in LLMs?
- Legend2440 1y ago
  They are far far more capable than anything your fellow computational linguists have come up with. As the saying goes, 'every time I fire a linguist, the performance of the speech recognizer goes up'
  - dunefox 1y ago
    1. Sadly, they are for most tasks, yes. 2. Linguist, not computational linguist. ;)
- suddenlybananas 1y ago
  Don't try and say anything pro-linguistics here, people are weirdly hostile if you think it's anything but probabilities.
  - PaulDavisThe1st 1y ago
    The interesting question is whether just a gigantic set of probabilities somehow captures things about language and cognition that we would not expect ...
sp1nningaway 1y ago
For me as a lay-person, the article is disjointed and kinda hard to follow. It's fascinating that all the quotes are emotional responses or about academic politics. Even now, they are suspicious of transformers and are bitter that they were wrong. No one seems happy that their field of research has been on an astonishing rocketship of progress in the last decade.
- dekhn 1y ago
  The way I see this is that for a long time there was an academic field that was working on parsing natural human language and it was influenced by some very smart people who had strong opinions. They focused mainly on symbolic approaches to parsing, rather than probabilistic. And there were some fairly strong assumptions about structure and meaning. Norvig wrote about this: https://norvig.com/chomsky.html https://norvig.com/chomsky.html and I think the article bears repeated, close reading. Unfortunately, because ML models went brr some time ago (Norvig was at the leading edge of this when he worked on the early google search engine and had access to huge amounts of data), we've since seen that probabilistic approaches produce excellent results, surpassing everything in the NLP space in terms of producing real-world sysems, without addressing any of the issues that the NLP folks believe are key (see https://en.wikipedia.org/wiki/Stochastic_parrot https://en.wikipedia.org/wiki/Stochastic_parrot and the referenced paper). Personally I would have preferred if the parrot paper hadn't also discussed environmental costs of LLMs, and focused entirely on the semantic issues associated with probabilistic models. I think there's a huge amount of jealousy in the NLP space that probabilistic methods worked so well, so fast (with transformers being the key innovation that improved metrics). And it's clear that even state-of-the-art probabilistic models lack features that NLP people expected. Repeatedly we have seen that probabilistic methods are the most effective way to make forward progress, provided you have enough data and good algorithms. It would be interesting to see the NLP folks try to come up with models that did anything near what a modern LLM can do.
  - mistrial9 1y ago
    > most effective way to make forward progress powerful response but.. "fit for what purposes" .. All of human writings are not functionally equivalent. This has been discussed at length. e.g. poetry versus factual reporting or summation..
teruakohatu 1y ago
I am in academia and worked in NLP although I would describe myself as NLP adjacent. I can confirm LLMs have essentially confined a good chunk of historical research into the bin. I suspect there are probably still a few PhD students working on traditional methods knowing full well a layman can do better using the mobile ChatGPT app. That said traditional NLP has its uses. Using the VADER model for sentiment analysis while flawed is vastly cheaper than LLMs to get a general idea. Traditional NLP is suitable for many tasks people are now spending a lot of money asking GPT to do just because they know GPT. I recently did an analysis on a large corpus and VADER was essentially free while the cloud costs to run a Llama based sentiment model was about $1000. I ran both because VADER costs nothing but minimal CPU time. NLP can be wrong but it can’t be jailbroken and it won’t make stuff up.
- qnleigh 1y ago
  How well did VADER correlate with Llama? Did you try any other methods intermediate between them?
- Cheer2171 1y ago
  That's because VADER is just a dictionary mapping each word to a single sentiment weight and adding it up with some basic logic for negations and such. There's an ocean of smaller NLP ML between that naive approach and LLMs. LLMs are trained to do everything. If all you need is a model trained to do sentiment analysis, using VADER over something like DistilBERT is NLP malpractice in 2025.
  - crowcroft 1y ago
    Price isn't a real issue in almost every imaginable use case either. Even a small open source model would outperform and you're going to get a lot of tokens per dollar with that.
  - teruakohatu 1y ago
    > using VADER over something like DistilBERT is NLP malpractice in 2025. Ouch. Was that necessary? I used $1000 worth of GPU credits and threw in VADER because it’s basically free both in time and credits. I usually do this on large dataset out of pure interest in how it correlates with expensive methods on English language text. I am well aware of how VADER works and its limitations, I am also aware of the limitations of all sentiment analysis.
jsemrau 1y ago
I was contrasting FiNER, GliNER, and Smolagents in a recent blog post on my substack and while the first two are fast and provide somewhat good results, running a LLM locally is 10x better easily.
- darkteflon 1y ago
  Would love to read that post - we’re considering using GliNER for discrete parts of our ingestion pipeline where we assumed it would be a great perf/$ drop-in for larger models.
  - philipkglass 1y ago
    This looks like the post: https://jdsemrau.substack.com/p/finer-gliner-and-smolagents-in-finance https://jdsemrau.substack.com/p/finer-gliner-and-smolagents-...
    - jsemrau 1y ago
      Thank you
softwaredoug 1y ago
I’m curious how have large language models impacted linguistics and particularly the idea of a universal grammar?
- canjobear 1y ago
  There's a lot of debate about it. Here's one view: https://arxiv.org/abs/2501.17047 https://arxiv.org/abs/2501.17047
  - suddenlybananas 1y ago
    This paper is awful. They bizarrely argue the fact that transformers are not very sensitive to word order as a positive of transformers despite the fact that's not how languages work. There's also this absurd passage. >However, a closer look at the statistical structure of language use reveals that word order contains surprisingly little information over and above lexical information. To see this intuitively, imagine we give you a set of words {dogs, bones, eat} without telling you the original order of the words. You can still reconstruct the meaning based entirely on (1) the meanings of the words in isolation and (2) your knowledge of how the world works—dogs usually eat bones; bones rarely eat dogs. Indeed, many languages show a high level of nondeterminism in word order (Futrell et al., 2015b; Koplenig et al., 2017), and word order cues are often redundant with meaning or case markers (Pijpops and Zehentner, 2022; Mahowald et al., 2023). The fact that word order is relatively uninformative in usage also partly explains why bag-of-words methods dominated NLP tasks until around 2020, consistently outperforming much more sophisticated approaches: it turns out that most of the information in sentences is in fact present in the bag of words. While it is certainly possible to guess that your interlocutor meant "dogs eat bones", the sentence "bones eat dogs" is entirely possible (if unlikely)! For example, imagine a moving skeleton in a video game or something. The idea that word order isn't vital to meaning is deeply unserious. (Of course there are languages where word order matters less, but there are still important rules about constituent structure, clausal embedding etc, which constrain word order).
    - canjobear 1y ago
vjerancrnjak 1y ago
CNNs were outperforming traditional methods on some tasks before 2017. Problem was that all of the low level tasks , like part of speech tagging, parsing, named entity recognition , etc. never resulted in a good summarizing system or translating system. Probabilistic graphical models worked a bit but not much. Transformers were a leap, where none of the low level tasks had to be done for high level ones. Pretty sure that equivalent leap happened in computer vision a bit before. People were fiddling with low level pattern matching and filters and then it was all obliterated with an end to end cnn .
- mistrial9 1y ago
  > never resulted in a good ... translating system that seems too broad > all obliterated with an end to end cnn you mixed your nouns.. what you were saying about transformers was about transformers.. that specifically replaced cnn. So,no
  - kadushka 1y ago
    In NLP, transformers replaced RNNs. In computer vision, CNNs replaced previous methods (e.g. feature descriptors), and recently got replaced by visual transformers, though modern CNNs are still pretty good.
    - mistrial9 1y ago
      yikes! yes typing too fast..
- ActorNightly 1y ago
  There was no leap in research. Everything had to do with availability of compute. Neural nets are quite old, and everyone knew that they were universal function approximators. The reason why models never took off was because it was very expensive to train a model even of a limited size. There was no real available hardware to do this on short of supercomputer clusters, which were just all cpus, and thus wildly inefficient. But any researcher back then would have told you that you can figure anything out with neural nets. Sometime in 2006, Nvidia realized that a lot of the graphics compute was just generic parallel compute and released Cuda. People started using graphics cards for compute. Then someone figured out you can actually train deep neural nets with decent speed. Transformers wasn't even that big of a leap. The paper makes it sound like its some sort of novel architecture - in essence, instead of inputweights to next layer, you do inputmatrix1, inputmatrix2, inputmatrix3, and multiply them together. And as you guessed this, to train it you need more hardware because now you have to train 3 matrices rather than just one. If we ever get like ASIC for ml, basically at a certain point, we will be able to iterate on architectures itself. The optimal LLM may be a combination of CNN,RNN, and Transformer blocks, all interwtined.
Animats 1y ago
"It helps to have tenure when something like this happens."
- simianwords 1y ago
  I wonder whether tenures are causing inefficiencies in the market? You might be encouraging someone to work on an outdated field without the correct incentives.
  - JohnKemeny 1y ago
    Just like having employees with experience, I guess. But tenured researchers are supposed to have some more protection specifically because they do research (and reach conclusions) on topics that people in leadership positions in society might not like.
o11c 1y ago
Has there been an LLM that reliably does not ignore the word "not"? Because I'm pretty sure that's a regression compared to most prior NLP.
- selcuka 1y ago
  > Has there been an LLM that reliably does not ignore the word "not"? Curious. I would expect most of them to get that right, unless it's an intentionally tricky question. Do you have an example?
  - o11c 1y ago
    It tends to happen for any prompt that calls for generating a piece of output for which there are many valid answers, but one is highly weighted and you want variety. Do you remember that meme a few years ago where people were asked to generate a color and then a hand tool, and most people immediately responded "erqunzzrebengyrnfgbarbsgubfrgjb"? (rot13+padding for those who haven't done this) This particular example is too small to regularly trip AIs, but as a general rule I do not consider it tricky to try to textually negative-prompt to remove a commonly-valid-but-not-currently-wanted response. (Obviously, if you manually tweak the weights to forbid something rather than using the word "not", this fails.) From my very rough observations, for models that fit on a local device, it typically starts to happen maybe 10% of the time when the prompt reaches 300 characters or so (specifying other parts of what you want); bigger models just need a bit more input before they fail. Reasoning models might be better, but we can watch them literally burn power running nonsensical variations through the thought pane so they're far from a sure answer. This happens in any context you can think of: from scratch or extending an existing work; single or list; information lookup, prose generation, code generation (consider "Extend this web app to do lengthy-description-of-some-task. Remember I am not using React you stupid piece of shit AI!").
ukuina 1y ago
Have we already forgotten what AlexNet did to Computer Vision as a research domain?
g42gregory 1y ago
My view is that "traditional" NLP will get re-incorporated into LLMs (or their successors) over time. We just didn't get to it yet. Appropriate inductive biases will only make LLMs better, faster and cheaper. There will always be trouble in LLM "paradise" and desire to take it to the next level. Use raw-accessed (highest performing) LLM, intensely, for coding and you will rack up $10-$20/hr bill. China is not supposed to have adequate GPUs at their disposal, - they will come up with smaller and more efficient models. Etc, etc, etc...
gitroom 1y ago
As someone who dropped out of NLP during the chaos, all this stuff honestly feels way too familiar - progress is cool but watching your work become pointless overnight stings hard.
whoaann_92 1y ago
As someone deeply involved in NLP, I’ve observed the field’s evolution: from decades of word counting and statistical methods to a decade of deep learning enabling “word arithmetic.” Now, with Generative AI, we’ve reached a new milestone, a universal NLP engine. IMHO, the path to scalability often involves using GPT models for prototyping and cold starts. They are incredible at generating synthetic data, which is invaluable for bootstrapping datasets and also data labelling of a given dataset. Once a sufficient dataset is available, training a transformer model becomes feasible for high-intensity data applications where the cost of using GPT would be prohibitive. GPT’s capabilities in data extraction and labeling are to me the killer applications, making it accessible for downstream tasks. This shift signifies that NLP is transitioning from a data science problem to an engineering one, focusing on building robust, scalable systems.
- GardenLetter27 1y ago
  Reminds me of the whole Chomsky vs. Norvig debate - https://norvig.com/chomsky.html https://norvig.com/chomsky.html
  - whoaann_92 1y ago
    Thanks for the link, just read it, and the Chomsky transcript. Chomsky wanted deep structure, Norvig bet on stats, but maybe Turing saw it coming, kids talk before they know grammar and so did the machines. It turns out we didn’t need to understand language to automate it.
thom 1y ago
I think the AI hype (much of which is justified) detracts from the fact that we have Actually Good NLP at last. I've worked on NL2SQL in both the before and after times, and it's still not a solved problem, but it's frustrating to talk to AI startup people who have never really thought deeply about named entity recognition, disambiguation etc. The tools are much, much better. The challenges and pitfalls remain much the same.
horsh1 1y ago
Unless we intend to surrender everything about human symbolic manipulations (all math, all proving, all computations, all programming) to llm in the nearest future, we still need some formal representations for engineering. The major part of tradidional NLP was about formal representations. We are still to see the efficient mining techniques to extract the formal representations and analyses back from LLM. How would we solve the traditional NLP problems, such as, for example, formalization of law corpus of a given country with LLM? As an approximation we can look at non-natural language processing, e.g. compiler technologies. How do we write an optimizing compiler on LLM technologies? How do we ensure stability, correctness and price? In a sence, the traditional NLP field has just doubled, not died. In addition to humans as language capable entities, who can not really explain how they use the language, we now also have LLM as another kind of language capable entities. Who in fact also can not explain anything. The only benefit is that it is cheaper to ask LLM the same question a million of times that a human.
Al-Khwarizmi 1y ago
As an NLP professor, yes, I think we're mostly screwed - saying LLMs are a dead end or not a big deal, like some of the interviewed say, is just wishful thinking. A lot of NLP tasks that were subject of active research for decades have just been wiped out. Ironically, the tasks that still aren't solved well by LLMs and can still have a few years of life in them are the most low-level ones, that had become unfashionable in the last 15 years or so - part-of-speech tagging, syntactic parsing, NER. Of course, they have lost a lot of importance as well: you no longer need them for user-oriented downstream tasks. But they may still get some use: for example NER for its own sake is used in biomedical domains, and parsing can be useful for scientific studies of language (say, language universals related to syntax, language evolution, etc.). Which is more than you can say about translation or summarization, which have been pretty much obsoleted by LLMs. Still, even with these tasks, NLP will go from broad applicability to niche. I'm not too worried for my livelihood at the moment (partly because I have tenure, and partly because the academic system works in such a way that zombie fields keep walking for quite long - there are still journals and conferences on the semantic web, which has been a zombie for who knows how long). But it's a pity: I got into this because it was fun and made an impact and now it seems most of my work is going to be irrelevant, like those semantic web researchers I used to look down at. I guess my consolation is that computers that really understand human language was the dream that got me into this in the first place, and it has been realized early. I can play with it and enjoy it while I sink into irrelevant research, I guess :/ Or try to pivot into discrete math or something.
- larodi 1y ago
  Let's admit it - overnight it became much much harder to be a convincing professor, given each student can use GPTs of all sorts to contradict or otherwise intimidate you. Only a seasoned professor knows the feeling of being bullied by a smart-ass student. Which brings down the total value, the incentive, to teach, and also given the avoidance GPTs silently imprint in students. I mean - why write a program/paper/research when the GPT can do it for you, and save you the suffering. The whole area of algorithms suddenly became more challenging, as now you also have to understand folding multi-dimensional spaces, and retell this all as a nice story for students to remember. We are very likely heading into some dark uncharted era for academia, which will very likely lead to academia shrinking massively. And given the talk of 'all science now happens in big corpos'... I can expect the universities to go back to the original state they started from - monasteries. Saying this all having spent 20+ years as part-time contributor to one such monastery.
cainxinth 1y ago
Lots of great quotes in this piece but this one stuck out for me: > TAL LINZEN: It’s sometimes confusing when we pretend that there’s a scientific conversation happening, but some of the people in the conversation have a stake in a company that’s potentially worth $50 billion.
deleted 1y ago
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mollerhoj 1y ago
Too bad Googles BERT gets all the credit, when the real innovation was ULMfit

278 points by mathgenius 1y ago

If Chomsky was writing papers in 2020 his paper would’ve been “language is all you need.” That is clearly not true and as the article points out wide scale very large forecasting models beat that hypothesis that you need an actual foundational structure for language in order to demonstrate intelligence when in fact is exactly the opposite. I’ve never been convinced by that hypothesis if for no other reason that we can demonstrate in the real world that intelligence is possible without linguistic structure. As we’re finding: solving the markov process iteratively is the foundation of intelligence out of that process emerges novel state transition processes - in some cases that’s novel communication methods that have structured mapping to state encoding inside the actor communications happen across species to various levels of fidelity but it is not the underlying mechanism of intelligence, it is an emerging behavior that allows for shared mental mapping and storage

aidenn0 1y ago
Some people will never be convinced that a machine demonstrates intelligence. This is because for a lot of people, intelligence exists a subjective experience that they have and the belief that others have it too is only inasmuch as others appear to be like the self.
- dekhn 1y ago
  This is why I want the field to go straight to building indistinguishable agents- specifically, you should be able to video chat with an avatar that is impossible to tell from a human. Then we can ask "if this is indistinguishable from a human, how can you be sure that anybody is intelligent?" Personally I suspect we can make zombies that appear indistinguishable from humans (limited to video chat; making a robot that appears human to a doctor would be hard) but that don't have self-consciousness or any subjective experience.
  - bsder 1y ago
    "There is considerable overlap between the intelligence of the smartest bears and the dumbest tourists." LLMs are not artificial intelligence but artificial stupidity. LLMs will happily hallucinate. LLMs will happily tell you total lies with complete confidence. LLMs will give you grammatically perfect completely vapid content. etc. And yet that is still better than what most humans could do in the same situation. We haven't proved that machines can have intelligence, but instead we are happily proving that most people, most of the time just aren't very intelligent at all.
    - JumpCrisscross 1y ago
      > yet that is still better than what most humans could do in the same situation Yup. A depressing takeaway from LLMs is most humans don’t demonstrate a drive to be curious and understand, but instead, to sort of muddle through most (economic) tasks.
    - vladms 1y ago
      > LLMs will happily hallucinate. LLMs will happily tell you total lies with complete confidence. Perhaps we should avoid anthropomorphizing them too much. LLMs don't inhabit a "real world" where they can experiment and learn. Their training data is their universe, and it's likely filled with conflicting, peculiar, and untestable information. Yes, the output is sometimes "a lie" if we apply it to our world, but in "their world" stuff is might be just strangely different. And it's not like the real world has only "hard simple truths" - quantum mechanics comes to mind about how strange stuff can be.
  - ActorNightly 1y ago
    Thats not really intelligence though.
- 6stringmerc 1y ago
  It is until proven otherwise because modern science still doesn’t have a consensus or standards or biological tests which can account for it. As in, highly “intelligent” people often lack “common sense” or fall prey to con artists. It’s pompous as shit to assert a black box mimicry constitutes intelligence. Wake me up when it can learn to play a guitar and write something as good as Bob Dylan and Tom Petty. Hint: we’ll both be dead before that happens.
  - aidenn0 1y ago
    I can't write something as good as Bob Dylan and Tom Petty. Ergo I'm not intelligent.
    - coffeeaddict1 1y ago
      This to me is a weak argument. You have the ability to appreciate and judge something as good as Bob Dylan and Tom Petty. That's what makes you intelligent.
      - motorest 1y ago
        > This to me is a weak argument. You have the ability to appreciate and judge something as good as Bob Dylan and Tom Petty. That's what makes you intelligent. What if you don't? Do you think that makes someone not intelligent? Think about it for a second.
        coffeeaddict1 1y ago
        Yes. If you do not possess the potential ability to judge other human beings and/or their work, you lack intelligence.
      - aidenn0 1y ago
        1. I'm sure if I were to ask an LLM for opinions on Dylan and Petty, it would provide them. 2. I don't know if this was the point the original was making, but I personally think Dylan is a bit overrated as a songwriter (and the one time I saw him live, he was only so-so as a performer, but I don't think that's exactly a hot take).
- meroes 1y ago
  It doesn’t mean they tie intelligence to subjective experience. Take digestion. Can a computer simulate digestion, yes. But no computer can “digest” if it’s just silicon in the corner of an office. There are two hurdles. The leap from simulating intelligence to intelligence, and the leap from intelligence to subjective experience. If the computer gets attached to a mechanism that physically breaks down organic material, that’s the first leap. If the computer gains a first person experience of that process, that’s the second. You can’t just short-circuit from simulates to does to has subjective experience. And the claim other humans don’t have subjective experience is such non-starter.
  - aidenn0 1y ago
    I think you're talking about consciousness rather than intelligence. While I do see people regularly distinguishing between simulation and reality for consciousness, I don't often see people make that distinction for intelligence. > And the claim other humans don’t have subjective experience is such non-starter. What about other primates? Other mammals? The smarter species of cephalopods? Certain many psychopaths seem to act as if they have this belief.
  - lupusreal 1y ago
    > And the claim other humans don’t have subjective experience is such non-starter. There is no empirical test for the subjective experience of consciousness. You can't even prove to anybody else that you have it. We assume other people experience as we ourselves do as a basic decency we extend to other humans. This is a good thing, but it's essentially faith not science. As for machines not having it, I'm fine with that assumption, but until there can be some sort of empirical test for it, it's not science. Thankfully, it's also not relevant to any engineering matter. Whether the machines have a subjective experience in any way comparable to our own doesn't touch any question about what demonstrable capabilities or limitations they have. We don't need to know if the computer has a ""soul"" to know if the computer can be a solution to any given engineering problem. Whether machines can have subjective experience shouldn't be considered an important question to engineers; let theologians waste their time fruitlessly debating that.
- shmel 1y ago
  How do they convince themselves that other people have intelligence too?
- simonw 1y ago
  It's called the AI effect: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect > The author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'."
  - qzw 1y ago
    > somebody figured out how to make a computer do something Well, I would argue that in most deterministic AI systems the thinking was all done by the AI researchers and then encoded for the computer. That’s why historically it’s been easy to say, “No, the machine isn’t doing any thinking, but only applying thinking that’s embedded within.” I think that line of argument becomes less obvious when you have learning systems where the behavior is training dependent. It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.
    - svieira 1y ago
      > It’s still fairly safe to argue that the best LLMs today are not ... thinking I agree completely. > But in another generation or two? It will become much harder to deny. Unless there is something ... categorically different about what an LLM does and in a generation or two we can articulate what that is (30 years of looking at something makes it easier to understand ... sometimes).
      - aorloff 1y ago
        Intelligence requires agency
    - otabdeveloper4 1y ago
      In many ways LLMs are a regression compared to what was before. They solve a huge class of problems quickly and cheaply, but they also have severe limitations that older methods didn't have. So no, it's not a linear progress story like in a sci-fi story.
- AndrewKemendo 1y ago
  Humans are basically incapable of recognizing that there’s something that’s more powerful than them They’re never going to actively collectively admit that that’s the case, because humans collectively are so so systematically arrogant and self possessed that they’re not even open to the possibility of being lower on the intelligence totem pole The only possible way forward for AI is to create the thing that everybody is so scared of so they can actually realize their place in the universe
  - skissane 1y ago
    > Humans are basically incapable of recognizing that there’s something that’s more powerful than them > They’re never going to actively collectively admit that that’s the case, because humans collectively are so so systematically arrogant and self possessed that they’re not even open to the possibility of being lower on the intelligence totem pole For most of human history, the clear majority of humans have believed in God(s), spirits, angels, bodhisattvas, etc - beings which by definition are above us on the “totem pole” - and although atheism is much more widespread today, I think it almost certainly remains a minority viewpoint at the global level. So I’m sceptical of your idea humans have some inherent unwillingness to believe in superhuman entities. From an atheist perspective, one might say that the (globally/historically) average human is so eager to believe in such entities, that if they don’t actually exist, they’ll imagine them and then convince themselves that their imaginings are entirely real. (Whereas, a theist might argue that the human eagerness to believe in such entities is better explained by their existence.)
    - AndrewKemendo 1y ago
      Religious people - depending on type - use the God as cosplay or as some kind of deus ex machina that they are merged with in their mind. Ask a deeply religious person about the separation between themselves and God and each tradition has their own version of being one with God or that they’ll become God etc… It’s all the same because it’s ultimately about their relationship with how they define their God - having a “religious experience” where you realize “you are a part of god and get benefit etc..” is frankly a requirement to be a religion In part that’s why it’s hard to call some versions of Buddhism religions and even harder for eg Hindus
- AIPedant 1y ago
  I will not be convinced a machine demonstrates intelligence until someone demonstrates a robot that can navigate 3D space as intelligently as, say, a cockroach. AFAICT we are still many years away from this, probably decades. A bunch of human language knowledge and brittle heuristics doesn't convince me at all. This ad hominem is really irritating. People have complained since Alan Turing that AI research ignores simpler intelligence, instead trying to bedazzle people with fancy tricks that convey the illusion of human intelligence. Still true today: lots of talk about o3's (exaggerated) ability to do fancy math, little talk about its appallingly bad general quantitative reasoning. The idea of "jagged AI" is unscientific horseshit designed to sweep this stuff under the rug.
- felipeerias 1y ago
  In the natural world, intelligence requires embodiment. And, depending on your point of view, consciousness. Modern AI exhibits neither of those characteristics.
emp17344 1y ago
> As we’re finding: solving the markov process iteratively is the foundation of intelligence No, the markov process allows LLMs to make connections between existing representations of human intelligence. LLMs are nothing without a data set developed by an existing intelligence.

criddell 1y ago

The field is natural language processing.

dang 1y ago
I think we can squeeze it in there. Thanks!

languagehacker 1y ago

Great seeing Ray Mooney (who I took a graduate class with) and Emily Bender (a colleague of many at the UT Linguistics Dept., and a regular visitor) sharing their honest reservations with AI and LLMs. I try to stay as far away from this stuff as possible because when the bottom falls out, it's going to have devastating effects for everyone involved. As a former computational linguist and someone who built similar tools at reasonable scale for largeish social media organizations in the teens, I learned the hard way not to trust the efficacy of these models or their ability to get the sort of reliability that a naive user would expect from them in practical application.

philomath_mn 1y ago
Curious what you are expecting when you say "bottom falls out". Are you expecting significant failures of large-scale systems? Or more a point where people recognize some flaw that you see in LLMs?
Legend2440 1y ago
They are far far more capable than anything your fellow computational linguists have come up with. As the saying goes, 'every time I fire a linguist, the performance of the speech recognizer goes up'
- dunefox 1y ago
  1. Sadly, they are for most tasks, yes. 2. Linguist, not computational linguist. ;)
suddenlybananas 1y ago
Don't try and say anything pro-linguistics here, people are weirdly hostile if you think it's anything but probabilities.
- PaulDavisThe1st 1y ago
  The interesting question is whether just a gigantic set of probabilities somehow captures things about language and cognition that we would not expect ...

sp1nningaway 1y ago

For me as a lay-person, the article is disjointed and kinda hard to follow. It's fascinating that all the quotes are emotional responses or about academic politics. Even now, they are suspicious of transformers and are bitter that they were wrong. No one seems happy that their field of research has been on an astonishing rocketship of progress in the last decade.

dekhn 1y ago
The way I see this is that for a long time there was an academic field that was working on parsing natural human language and it was influenced by some very smart people who had strong opinions. They focused mainly on symbolic approaches to parsing, rather than probabilistic. And there were some fairly strong assumptions about structure and meaning. Norvig wrote about this: https://norvig.com/chomsky.html https://norvig.com/chomsky.html and I think the article bears repeated, close reading. Unfortunately, because ML models went brr some time ago (Norvig was at the leading edge of this when he worked on the early google search engine and had access to huge amounts of data), we've since seen that probabilistic approaches produce excellent results, surpassing everything in the NLP space in terms of producing real-world sysems, without addressing any of the issues that the NLP folks believe are key (see https://en.wikipedia.org/wiki/Stochastic_parrot https://en.wikipedia.org/wiki/Stochastic_parrot and the referenced paper). Personally I would have preferred if the parrot paper hadn't also discussed environmental costs of LLMs, and focused entirely on the semantic issues associated with probabilistic models. I think there's a huge amount of jealousy in the NLP space that probabilistic methods worked so well, so fast (with transformers being the key innovation that improved metrics). And it's clear that even state-of-the-art probabilistic models lack features that NLP people expected. Repeatedly we have seen that probabilistic methods are the most effective way to make forward progress, provided you have enough data and good algorithms. It would be interesting to see the NLP folks try to come up with models that did anything near what a modern LLM can do.
- mistrial9 1y ago
  > most effective way to make forward progress powerful response but.. "fit for what purposes" .. All of human writings are not functionally equivalent. This has been discussed at length. e.g. poetry versus factual reporting or summation..

teruakohatu 1y ago

I am in academia and worked in NLP although I would describe myself as NLP adjacent. I can confirm LLMs have essentially confined a good chunk of historical research into the bin. I suspect there are probably still a few PhD students working on traditional methods knowing full well a layman can do better using the mobile ChatGPT app. That said traditional NLP has its uses. Using the VADER model for sentiment analysis while flawed is vastly cheaper than LLMs to get a general idea. Traditional NLP is suitable for many tasks people are now spending a lot of money asking GPT to do just because they know GPT. I recently did an analysis on a large corpus and VADER was essentially free while the cloud costs to run a Llama based sentiment model was about $1000. I ran both because VADER costs nothing but minimal CPU time. NLP can be wrong but it can’t be jailbroken and it won’t make stuff up.

qnleigh 1y ago
How well did VADER correlate with Llama? Did you try any other methods intermediate between them?
Cheer2171 1y ago
That's because VADER is just a dictionary mapping each word to a single sentiment weight and adding it up with some basic logic for negations and such. There's an ocean of smaller NLP ML between that naive approach and LLMs. LLMs are trained to do everything. If all you need is a model trained to do sentiment analysis, using VADER over something like DistilBERT is NLP malpractice in 2025.
- crowcroft 1y ago
  Price isn't a real issue in almost every imaginable use case either. Even a small open source model would outperform and you're going to get a lot of tokens per dollar with that.
- teruakohatu 1y ago
  > using VADER over something like DistilBERT is NLP malpractice in 2025. Ouch. Was that necessary? I used $1000 worth of GPU credits and threw in VADER because it’s basically free both in time and credits. I usually do this on large dataset out of pure interest in how it correlates with expensive methods on English language text. I am well aware of how VADER works and its limitations, I am also aware of the limitations of all sentiment analysis.

jsemrau 1y ago

I was contrasting FiNER, GliNER, and Smolagents in a recent blog post on my substack and while the first two are fast and provide somewhat good results, running a LLM locally is 10x better easily.

darkteflon 1y ago
Would love to read that post - we’re considering using GliNER for discrete parts of our ingestion pipeline where we assumed it would be a great perf/$ drop-in for larger models.
- philipkglass 1y ago
  This looks like the post: https://jdsemrau.substack.com/p/finer-gliner-and-smolagents-in-finance https://jdsemrau.substack.com/p/finer-gliner-and-smolagents-...
  - jsemrau 1y ago
    Thank you

softwaredoug 1y ago

I’m curious how have large language models impacted linguistics and particularly the idea of a universal grammar?

canjobear 1y ago
There's a lot of debate about it. Here's one view: https://arxiv.org/abs/2501.17047 https://arxiv.org/abs/2501.17047
- suddenlybananas 1y ago
  This paper is awful. They bizarrely argue the fact that transformers are not very sensitive to word order as a positive of transformers despite the fact that's not how languages work. There's also this absurd passage. >However, a closer look at the statistical structure of language use reveals that word order contains surprisingly little information over and above lexical information. To see this intuitively, imagine we give you a set of words {dogs, bones, eat} without telling you the original order of the words. You can still reconstruct the meaning based entirely on (1) the meanings of the words in isolation and (2) your knowledge of how the world works—dogs usually eat bones; bones rarely eat dogs. Indeed, many languages show a high level of nondeterminism in word order (Futrell et al., 2015b; Koplenig et al., 2017), and word order cues are often redundant with meaning or case markers (Pijpops and Zehentner, 2022; Mahowald et al., 2023). The fact that word order is relatively uninformative in usage also partly explains why bag-of-words methods dominated NLP tasks until around 2020, consistently outperforming much more sophisticated approaches: it turns out that most of the information in sentences is in fact present in the bag of words. While it is certainly possible to guess that your interlocutor meant "dogs eat bones", the sentence "bones eat dogs" is entirely possible (if unlikely)! For example, imagine a moving skeleton in a video game or something. The idea that word order isn't vital to meaning is deeply unserious. (Of course there are languages where word order matters less, but there are still important rules about constituent structure, clausal embedding etc, which constrain word order).
  - canjobear 1y ago

vjerancrnjak 1y ago

CNNs were outperforming traditional methods on some tasks before 2017. Problem was that all of the low level tasks , like part of speech tagging, parsing, named entity recognition , etc. never resulted in a good summarizing system or translating system. Probabilistic graphical models worked a bit but not much. Transformers were a leap, where none of the low level tasks had to be done for high level ones. Pretty sure that equivalent leap happened in computer vision a bit before. People were fiddling with low level pattern matching and filters and then it was all obliterated with an end to end cnn .

mistrial9 1y ago
> never resulted in a good ... translating system that seems too broad > all obliterated with an end to end cnn you mixed your nouns.. what you were saying about transformers was about transformers.. that specifically replaced cnn. So,no
- kadushka 1y ago
  In NLP, transformers replaced RNNs. In computer vision, CNNs replaced previous methods (e.g. feature descriptors), and recently got replaced by visual transformers, though modern CNNs are still pretty good.
  - mistrial9 1y ago
    yikes! yes typing too fast..
ActorNightly 1y ago
There was no leap in research. Everything had to do with availability of compute. Neural nets are quite old, and everyone knew that they were universal function approximators. The reason why models never took off was because it was very expensive to train a model even of a limited size. There was no real available hardware to do this on short of supercomputer clusters, which were just all cpus, and thus wildly inefficient. But any researcher back then would have told you that you can figure anything out with neural nets. Sometime in 2006, Nvidia realized that a lot of the graphics compute was just generic parallel compute and released Cuda. People started using graphics cards for compute. Then someone figured out you can actually train deep neural nets with decent speed. Transformers wasn't even that big of a leap. The paper makes it sound like its some sort of novel architecture - in essence, instead of inputweights to next layer, you do inputmatrix1, inputmatrix2, inputmatrix3, and multiply them together. And as you guessed this, to train it you need more hardware because now you have to train 3 matrices rather than just one. If we ever get like ASIC for ml, basically at a certain point, we will be able to iterate on architectures itself. The optimal LLM may be a combination of CNN,RNN, and Transformer blocks, all interwtined.

Animats 1y ago

"It helps to have tenure when something like this happens."

simianwords 1y ago
I wonder whether tenures are causing inefficiencies in the market? You might be encouraging someone to work on an outdated field without the correct incentives.
- JohnKemeny 1y ago
  Just like having employees with experience, I guess. But tenured researchers are supposed to have some more protection specifically because they do research (and reach conclusions) on topics that people in leadership positions in society might not like.

o11c 1y ago

Has there been an LLM that reliably does not ignore the word "not"? Because I'm pretty sure that's a regression compared to most prior NLP.

selcuka 1y ago
> Has there been an LLM that reliably does not ignore the word "not"? Curious. I would expect most of them to get that right, unless it's an intentionally tricky question. Do you have an example?
- o11c 1y ago
  It tends to happen for any prompt that calls for generating a piece of output for which there are many valid answers, but one is highly weighted and you want variety. Do you remember that meme a few years ago where people were asked to generate a color and then a hand tool, and most people immediately responded "erqunzzrebengyrnfgbarbsgubfrgjb"? (rot13+padding for those who haven't done this) This particular example is too small to regularly trip AIs, but as a general rule I do not consider it tricky to try to textually negative-prompt to remove a commonly-valid-but-not-currently-wanted response. (Obviously, if you manually tweak the weights to forbid something rather than using the word "not", this fails.) From my very rough observations, for models that fit on a local device, it typically starts to happen maybe 10% of the time when the prompt reaches 300 characters or so (specifying other parts of what you want); bigger models just need a bit more input before they fail. Reasoning models might be better, but we can watch them literally burn power running nonsensical variations through the thought pane so they're far from a sure answer. This happens in any context you can think of: from scratch or extending an existing work; single or list; information lookup, prose generation, code generation (consider "Extend this web app to do lengthy-description-of-some-task. Remember I am not using React you stupid piece of shit AI!").

ukuina 1y ago

Have we already forgotten what AlexNet did to Computer Vision as a research domain?

g42gregory 1y ago

My view is that "traditional" NLP will get re-incorporated into LLMs (or their successors) over time. We just didn't get to it yet. Appropriate inductive biases will only make LLMs better, faster and cheaper. There will always be trouble in LLM "paradise" and desire to take it to the next level. Use raw-accessed (highest performing) LLM, intensely, for coding and you will rack up $10-$20/hr bill. China is not supposed to have adequate GPUs at their disposal, - they will come up with smaller and more efficient models. Etc, etc, etc...

gitroom 1y ago

As someone who dropped out of NLP during the chaos, all this stuff honestly feels way too familiar - progress is cool but watching your work become pointless overnight stings hard.

whoaann_92 1y ago

As someone deeply involved in NLP, I’ve observed the field’s evolution: from decades of word counting and statistical methods to a decade of deep learning enabling “word arithmetic.” Now, with Generative AI, we’ve reached a new milestone, a universal NLP engine. IMHO, the path to scalability often involves using GPT models for prototyping and cold starts. They are incredible at generating synthetic data, which is invaluable for bootstrapping datasets and also data labelling of a given dataset. Once a sufficient dataset is available, training a transformer model becomes feasible for high-intensity data applications where the cost of using GPT would be prohibitive. GPT’s capabilities in data extraction and labeling are to me the killer applications, making it accessible for downstream tasks. This shift signifies that NLP is transitioning from a data science problem to an engineering one, focusing on building robust, scalable systems.

GardenLetter27 1y ago
Reminds me of the whole Chomsky vs. Norvig debate - https://norvig.com/chomsky.html https://norvig.com/chomsky.html
- whoaann_92 1y ago
  Thanks for the link, just read it, and the Chomsky transcript. Chomsky wanted deep structure, Norvig bet on stats, but maybe Turing saw it coming, kids talk before they know grammar and so did the machines. It turns out we didn’t need to understand language to automate it.

thom 1y ago

I think the AI hype (much of which is justified) detracts from the fact that we have Actually Good NLP at last. I've worked on NL2SQL in both the before and after times, and it's still not a solved problem, but it's frustrating to talk to AI startup people who have never really thought deeply about named entity recognition, disambiguation etc. The tools are much, much better. The challenges and pitfalls remain much the same.

horsh1 1y ago

Unless we intend to surrender everything about human symbolic manipulations (all math, all proving, all computations, all programming) to llm in the nearest future, we still need some formal representations for engineering. The major part of tradidional NLP was about formal representations. We are still to see the efficient mining techniques to extract the formal representations and analyses back from LLM. How would we solve the traditional NLP problems, such as, for example, formalization of law corpus of a given country with LLM? As an approximation we can look at non-natural language processing, e.g. compiler technologies. How do we write an optimizing compiler on LLM technologies? How do we ensure stability, correctness and price? In a sence, the traditional NLP field has just doubled, not died. In addition to humans as language capable entities, who can not really explain how they use the language, we now also have LLM as another kind of language capable entities. Who in fact also can not explain anything. The only benefit is that it is cheaper to ask LLM the same question a million of times that a human.

Al-Khwarizmi 1y ago

As an NLP professor, yes, I think we're mostly screwed - saying LLMs are a dead end or not a big deal, like some of the interviewed say, is just wishful thinking. A lot of NLP tasks that were subject of active research for decades have just been wiped out. Ironically, the tasks that still aren't solved well by LLMs and can still have a few years of life in them are the most low-level ones, that had become unfashionable in the last 15 years or so - part-of-speech tagging, syntactic parsing, NER. Of course, they have lost a lot of importance as well: you no longer need them for user-oriented downstream tasks. But they may still get some use: for example NER for its own sake is used in biomedical domains, and parsing can be useful for scientific studies of language (say, language universals related to syntax, language evolution, etc.). Which is more than you can say about translation or summarization, which have been pretty much obsoleted by LLMs. Still, even with these tasks, NLP will go from broad applicability to niche. I'm not too worried for my livelihood at the moment (partly because I have tenure, and partly because the academic system works in such a way that zombie fields keep walking for quite long - there are still journals and conferences on the semantic web, which has been a zombie for who knows how long). But it's a pity: I got into this because it was fun and made an impact and now it seems most of my work is going to be irrelevant, like those semantic web researchers I used to look down at. I guess my consolation is that computers that really understand human language was the dream that got me into this in the first place, and it has been realized early. I can play with it and enjoy it while I sink into irrelevant research, I guess :/ Or try to pivot into discrete math or something.

larodi 1y ago
Let's admit it - overnight it became much much harder to be a convincing professor, given each student can use GPTs of all sorts to contradict or otherwise intimidate you. Only a seasoned professor knows the feeling of being bullied by a smart-ass student. Which brings down the total value, the incentive, to teach, and also given the avoidance GPTs silently imprint in students. I mean - why write a program/paper/research when the GPT can do it for you, and save you the suffering. The whole area of algorithms suddenly became more challenging, as now you also have to understand folding multi-dimensional spaces, and retell this all as a nice story for students to remember. We are very likely heading into some dark uncharted era for academia, which will very likely lead to academia shrinking massively. And given the talk of 'all science now happens in big corpos'... I can expect the universities to go back to the original state they started from - monasteries. Saying this all having spent 20+ years as part-time contributor to one such monastery.

cainxinth 1y ago

Lots of great quotes in this piece but this one stuck out for me: > TAL LINZEN: It’s sometimes confusing when we pretend that there’s a scientific conversation happening, but some of the people in the conversation have a stake in a company that’s potentially worth $50 billion.

deleted 1y ago

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mollerhoj 1y ago

Too bad Googles BERT gets all the credit, when the real innovation was ULMfit