4 ms·

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 the

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 ...
- jdgoesmarching 1y ago
  Maybe they could’ve tried to say something pro-linguistics, but the comment was entirely anti-LLM.
  - suddenlybananas 1y ago
    God forbid someone not bow before the almighty LLM
- parpfish 1y ago
  Over my years in academia, I noticed that the linguistics departments were always the most fiercely ideological. Almost every comment of a talk would be get contested by somebody from the audience. It was annoying, but as a psych guy I was also jealous of them for having such clearly articulated theoretical frameworks. It really helped them develop cohesive lines of research to delineate the workings of each theory
  - throwaway314155 1y ago
    Are there really all that many parallels between linguistics (the study of langauge) and computational-linguistics/NLP (subject of discussion)?
    - Legend2440 1y ago
      Computational linguistics, yes - it is the application of linguistics to computers. Modern NLP, not really - it's all based around statistical modeling with very little linguistics.
      - dunefox 1y ago
        IME, computational linguistics is just NLP and has been for 10 years or so.
- motorest 1y ago
  > Don't try and say anything pro-linguistics here, (...) Shit-talking LLMs without providing any basis or substance is not what I would call "pro-linguistics". It just sounds like petty spiteful behavior, lashing out out of frustration for rendering old models obsolete.
  - suddenlybananas 1y ago
    From a scientific explanatory perspective, the old models are not obsolete because they are explanatory whereas LLMs do not explain anything about human linguistic behaviour.
    - motorest 1y ago
      On one hand you have models which you argue are explanatory, but arguably do not work. On the other hand, you have models that not only work but took the world by storm, and may or may not be made explanatory. You either invest more work getting one explanatory model to work, or invest more work getting a working model to become explanatory. What do you think is the fruitful research path?
      - suddenlybananas 1y ago
        What do you mean "work"? Their goal is not to be some general purpose AI. (To be clear I'm talking narrowly about computational linguistics, not old fashioned NLP more broadly).
JumpCrisscross 1y ago
> 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 But…they work. Linguistics as a science is still solid. But as a practical exercise, it seems to be moot other than for finding niches where LLMs are too pricey.
ahnick 1y ago
How exactly is the bottom going to fall out? And are you really trying to present that you have practical experience building comparable tools to an LLM prior to the Transformer paper being written? Now, there does appear to be some shenanigans going on with circular financing involving MSFT, NVIDIA, and SMCI (https://x.com/DarioCpx/status/1917757093811216627 https://x.com/DarioCpx/status/1917757093811216627), but the usefulness of all the modern LLMs is undeniable. Given the state of the global economy and the above financial engineering issues I would not be surprised that at some point there isn't a contraction and the AI hype settles down a bit. With that said, LLMs could be made illegal and people would still continue running open source models indefinitely and organizations will build proprietary models in secret, b/c LLMs are that good. Since we are throwing out predictions, I'll throw one out. Demand for LLMs to be more accurate will bring methods like formal verification to the forefront and I predict eventually model/agents will start to be able to formalize solved problems into proofs using formal verification techniques to guarantee correctness. At that point you will be able to trust the outputs for things the model "knows" (i.e. has proved) and use the probably correct answers the model spits out as we currently do today. Probably something like the following flow: 1) Users enter prompts 2) Model answers questions and feeds those conversations to another model/program 3) Offline this other model uses formal verification techniques to try and reduce the answers to a formal proof. 4) The formal proofs are fed back into the first model's memory and then it uses those answers going forward. 5) Future questions that can be mapped to these formalized proofs can now be answered with almost no cost and are guaranteed to be correct.
- throwaway314155 1y ago
  > And are you really trying to present that you have practical experience building comparable tools to an LLM prior to the Transformer paper being written? I believe (could be wrong) they were talking about their prior GOFAI/NLP experience when referencing scaling systems. In any case, is it really necessary to be so harsh about over-confidence and then go on to predict the future of solving hallucinations with your formal verification ideas? Talk is cheap. Show me the code.
  - baq 1y ago

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 ...
jdgoesmarching 1y ago
Maybe they could’ve tried to say something pro-linguistics, but the comment was entirely anti-LLM.
- suddenlybananas 1y ago
  God forbid someone not bow before the almighty LLM
parpfish 1y ago
Over my years in academia, I noticed that the linguistics departments were always the most fiercely ideological. Almost every comment of a talk would be get contested by somebody from the audience. It was annoying, but as a psych guy I was also jealous of them for having such clearly articulated theoretical frameworks. It really helped them develop cohesive lines of research to delineate the workings of each theory
- throwaway314155 1y ago
  Are there really all that many parallels between linguistics (the study of langauge) and computational-linguistics/NLP (subject of discussion)?
  - Legend2440 1y ago
    Computational linguistics, yes - it is the application of linguistics to computers. Modern NLP, not really - it's all based around statistical modeling with very little linguistics.
    - dunefox 1y ago
      IME, computational linguistics is just NLP and has been for 10 years or so.
motorest 1y ago
> Don't try and say anything pro-linguistics here, (...) Shit-talking LLMs without providing any basis or substance is not what I would call "pro-linguistics". It just sounds like petty spiteful behavior, lashing out out of frustration for rendering old models obsolete.
- suddenlybananas 1y ago
  From a scientific explanatory perspective, the old models are not obsolete because they are explanatory whereas LLMs do not explain anything about human linguistic behaviour.
  - motorest 1y ago
    On one hand you have models which you argue are explanatory, but arguably do not work. On the other hand, you have models that not only work but took the world by storm, and may or may not be made explanatory. You either invest more work getting one explanatory model to work, or invest more work getting a working model to become explanatory. What do you think is the fruitful research path?
    - suddenlybananas 1y ago
      What do you mean "work"? Their goal is not to be some general purpose AI. (To be clear I'm talking narrowly about computational linguistics, not old fashioned NLP more broadly).

JumpCrisscross 1y ago

> 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 But…they work. Linguistics as a science is still solid. But as a practical exercise, it seems to be moot other than for finding niches where LLMs are too pricey.

ahnick 1y ago

How exactly is the bottom going to fall out? And are you really trying to present that you have practical experience building comparable tools to an LLM prior to the Transformer paper being written? Now, there does appear to be some shenanigans going on with circular financing involving MSFT, NVIDIA, and SMCI (https://x.com/DarioCpx/status/1917757093811216627 https://x.com/DarioCpx/status/1917757093811216627), but the usefulness of all the modern LLMs is undeniable. Given the state of the global economy and the above financial engineering issues I would not be surprised that at some point there isn't a contraction and the AI hype settles down a bit. With that said, LLMs could be made illegal and people would still continue running open source models indefinitely and organizations will build proprietary models in secret, b/c LLMs are that good. Since we are throwing out predictions, I'll throw one out. Demand for LLMs to be more accurate will bring methods like formal verification to the forefront and I predict eventually model/agents will start to be able to formalize solved problems into proofs using formal verification techniques to guarantee correctness. At that point you will be able to trust the outputs for things the model "knows" (i.e. has proved) and use the probably correct answers the model spits out as we currently do today. Probably something like the following flow: 1) Users enter prompts 2) Model answers questions and feeds those conversations to another model/program 3) Offline this other model uses formal verification techniques to try and reduce the answers to a formal proof. 4) The formal proofs are fed back into the first model's memory and then it uses those answers going forward. 5) Future questions that can be mapped to these formalized proofs can now be answered with almost no cost and are guaranteed to be correct.

throwaway314155 1y ago
> And are you really trying to present that you have practical experience building comparable tools to an LLM prior to the Transformer paper being written? I believe (could be wrong) they were talking about their prior GOFAI/NLP experience when referencing scaling systems. In any case, is it really necessary to be so harsh about over-confidence and then go on to predict the future of solving hallucinations with your formal verification ideas? Talk is cheap. Show me the code.
- baq 1y ago