15 ms·

Emergent abilities of large language models

157 points by tlb 4y ago

jmull 4y ago
They use the word "emergent" to mean: not present in small models but is present in large models I don't like that term, because it implies something deeper, so people immediately misinterpret this kind of information. (Just read this thread.)
- Chirono 4y ago
  What would you propose instead?
  - jmull 4y ago
    Just "abilities present in large models not present in small models". This also makes clear its relative nature (that is, what "large" and "small" mean), which the use of the word "emergent" obscures.
    - 1123581321 4y ago
      That’s much longer and raises more questions (what is large and small, does this gradually or discretely appear.)
    - LawTalkingGuy 4y ago
      There's things which small models can't do, and where obviously a large model works better. Not emergence, I agree. I think they're using emergent for a different category of things - still things that a small model couldn't do, but specifically which a large model can just do without any of the work we thought we'd have to do for that ability. Or at least, that's how I see emergent being a useful term, both in a behavior and a value context.
- bangkoksbest 4y ago
  "Emergent" as used here is akin to how physicists use the word. "Emergent" as you interpret it (with the implication of something deeper) is more akin to how philosophers use the word. Almost every discussion I've read in philosophy over emergence is tedious and hard to see any use, whereas in physics it's intuitive and descriptively useful. My sense is that it's better to follow the physics style and stress there's no metaphysics implied.
  - kerkeslager 4y ago
    > "Emergent" as used here is akin to how physicists use the word. "Emergent" as you interpret it (with the implication of something deeper) is more akin to how philosophers use the word. No, if you had read even the first paragraph of the article, you'd have seen that the person you're responding to isn't "interpreting" the article, they're quoting the article. Don't correct people when you don't know what you're talking about. You haven't read the article so you're in no position to talk about the article. This is the level of discussion I've come to expect from Hacker News, unfortunately.
    - bangkoksbest 4y ago
      What are you talking about? I read the article, there is literally no implied metaphysics about deeper emergence in the article at all.
  - JanisErdmanis 4y ago
    I agree with physics notion of emergence used in the article. A single water molecule does not make water it’s properties of density, viscosity, specific heat and etc. There needs to be large number of molecules when such properties emerge.
  - cowl 4y ago
    Where do you see this kind of Meaning in physics? In physics we have of example the Emergence of fluid dynamics and is clearly defined as a behaviour or property of system that does not derive simply from the sum of combination of it's subsystems. (it's not observed in small quantities of particles and it's not inherent to the particles) We have for example the Emergent behaviour of a school of fish Swimming in a new behaviour that is not observed on few fish etc. That is the mental model of "Emergence" most of us are familiar with. A new behaviour that can not be observed or "explained by" the sum of the parts of system. There is no metaphysics here. What is observed here is not new or different behaviour, it's simple a more precise and extensive version of the same behaviour. What is observed here is like comparing a 2Mpix resolution camera with a 12Mpix low light camera and saying that seeing more details in photo taken at 8pm is an emergent property of the camera because those details where not there in the previous version.
- cwmoore 4y ago
  The implication of something deeper is present in emerging features of the larger models. What is that if not emergent intelligence? Can you expand?
m3kw9 4y ago
Why is there a surprise when a larger model works better? And call it emergent?
- typon 4y ago
  There's no a priori reason to assume that models would see a step function improvement in abilities as their size increases. Perhaps the null hypothesis would be that we'd observe a linear increase in capability. Empirical evidence shows that's not the case, and that's why studying this phenomena is interesting.
  - deleted 4y ago
    [deleted]
  - majormajor 4y ago
    The judgement criteria itself looks step-like for many of these things. Word unscrambling, for instance: does a 15 percentage point increase in accuracy suggest that you're 15x better at it (from close to 0 to 15%ish)? Or could you do that by being modestly better, but that's just enough to cross the threshold to solve the simplest 15% of words in the task challenge you're being graded on? We're also using a log scale on the X axis there but a linear one for the Y axis which amplifies the "steppiness"
    - bob1029 4y ago
      This is a fair point. Things like arithmetic either work or they don't. Is partial credit issued as a linear interpolation between the predicted value and expected value? If not, then you necessarily will have step-like behavior in the data.
- int_19h 4y ago
  The surprise is that it's qualitatively better. Like being able to talk and follow instructions where smaller models couldn't do either at all.
lsy 4y ago
There is something about this that seems unscientific. What defines an "ability"? It sort of feels like the Guinness-Book type records for "most laps done around the Washington monument with a duck on your head". Who is making up these ability tests and why? As an example, I clicked through to "english russian proverbs" which gives the model an english input and four russian proverbs, and asks the model to pick the closest one in meaning. There are 80 of these questions. My question is "why this task? why 80? why 4 options?" The authors give some nonspecific reasoning in the github page, but it really feels like people are out there creating random tasks as they think of them, running them all against models, and the ones that do better get added to a list to make headlines like "137 emergent abilities of language models", when the number of abilities models can do seems dominated by the number of "abilities" that have been invented, rather than any property of the model. I suspect that if we continued to use the same process to create twice as many "abilities", we would see something like twice as many that models can do. The fact that the models get better at these constructed tasks when they have more parameters also seems like a no-brainer. You are adding coefficients to a function. A function with 1 coefficient will not be able to describe much. A function with 2 describes more. If you are memorizing the internet, having more coefficients means you can memorize more stuff, and even stuff with only a few examples can be better-fit in the billions of parameters. With growing training set size, and companies hiding their data, we also run into the issue of nobody really knowing what is in the training set. Did someone clone the big-bench repository and it got sucked into a training set and that's why the model does really well on its tasks after a certain number of parameters?
- zamfi 4y ago
  I can't speak to this particular paper, but the field of NLP is full of these kinds of tasks, it's in some sense a historical accident of where LLMs originated. These tasks are all noisy, all pretty messy, but the interesting part here is not that performance improves on these constructed tasks -- NLP researchers have been improving performance on constructed tasks for decades, by customizing models and curating datasets. Rather, the interesting part is that performance improves despite no specific model or data shifts except for scale. It seems that you're arguing two things: first, that these tasks aren't that interesting -- on that we agree. But, second, that it's obvious that performance would improve on arbitrary tasks because of memorization and an unknown training set -- but this is easy to validate: make up a new constructed task, and see how it performs.
jwie 4y ago
They have no other abilities in the first place. The adjectives are just as meaningless. "Ask" it about academic articles and their authors for {topic} and to summarize a body of research. The names might even be real people, but they won't have written the article, which probably doesn't exist. What it will do is sound like an article title, and to anyone unfamiliar with that body of work, will sound plausible. If your area of inquiry doesn't require valuable or reliable information as an output, sure I guess they have value. But for anything that's hard and matters, it's a parlor trick unfit for use.
- Nevermark 4y ago
  The models are trained to respond with “guesses” too confidently, due at at least three aspects of using online discussions as examples: 1. The obvious one: Lots of examples of humans with no clue, talking like they are absolute experts. 2. Humans without anything to contribute to discussions are not pressured to say something. They may be “there” in the discussion, but the model can’t learn from their silence. Unlike how we are forcing these uninformed outputs from the models. They need balanced examples of verbally expressed knowledge modesty to do this better. 3. Human’s implicit factoid omniscience, while they are online, given Google access, creates expectations that the non-Googling model can’t match.
  - LawTalkingGuy 4y ago
    > They need balanced examples of verbally expressed knowledge modesty to do this better. This is generally pretty easy, with ChatGPT at least, in my experience. Langchain uses this prompt: "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know." Also, just don't ask it for things it couldn't possibly know without making it up like medical citations. This is more for where you want your "AI" assistant to say it does not know how to book a reservation rather than pretend that it can.
    - Nevermark 4y ago
pl90087 4y ago
[flagged]
- gpderetta 4y ago
  An LLM-based classifier would be great at this task! And you can ask ChatGPT to write the extension for you!
- mecsred 4y ago
  Maybe you should ask ChatGPT to write you one.
mikewarot 4y ago
It would be interesting to see if these abilities were to go away if you subjected the large model to drop-outs as you continued training, until it were reduced to the size of the small model. I think once an "ability" is learned by the model, it is useful to help compress information, and is more likely than not (>50%) to be retained.
amelius 4y ago
> In Emergent abilities of large language models, we defined an emergent ability as an ability that is “not present in small models but is present in large models.” Shouldn't it be defined as the ability of an LLM to do stuff that it was not explicitly trained to do (i.e. not a direct reproduction of what is in the training data)?
jschveibinz 4y ago
Some basic propositions to consider as I am forming my opinion on this: 1. A static (relative) set of things is in one state until something acts upon the set. 2. A thing can have memory, or characteristics. 3. A single thing can appear differently to an observer depending upon the perspective of the observer. 4. Emergence is the appearance of a collection of things that is dependent upon the action and or interaction of the set of things and their characteristics, any externals (the environment) AND the perspective of the observer. Am I missing anything in my framing of the question?
olodus 4y ago
Off topic but the concept of "emergent behavior" is just so cool. It is like modern adventure and exploration. No one really knows what things emerge when we run models with emergent behavior further than we have yet seen and together with other things it hasn't yet interacted with. We make guesses, further models, we gain deeper understanding...
svnt 4y ago
In what technical way are these properties emergent? It seems that stated another way they are simply a natural consequence of more accurate language prediction and therefore not emergent except in how we perceive them.
- kerkeslager 4y ago
  Because they defined the word "emergent" in a way that is misleading, likely to make their study seem more interesting. To quote the article: > [W]e defined an emergent ability as an ability that is “not present in small models but is present in large models.” It's not a bad study but using that word in that way is extremely poor communication, which has predictably been seized upon by every breathless nontechnical AI enthusiast.
- hoseja 4y ago
  Isn't that what emergence is?
- Nevermark 4y ago
  > … they are simply a natural consequence of more … That’s what emergent means. Something scales quantitatively, and after reaching certain levels, without any other special prompting, something else changes qualitatively. Presumably we recognize these things, then learn something after we have investigated and found out what the new mode of operation is, why it needed what it did to emerge, and possibly find new efficiencies or other ways to amplify the emergent effects.
- nivenkos 4y ago
  Because it's not inherent to the system itself. For example there's nothing inherent to token prediction to make it capable of doing arithmetic and you wouldn't expect a small/briefly trained model to manage it. But if learning a very large model on a huge dataset leads to it "learning" the decimal number system and arithmetic via token prediction then that is an emergent capability. It's just like the Chinese Room thought experiment - https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thought_experiment https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thou...
cultureswitch 4y ago
Love how "German" is an emergent property
didnotreadit 4y ago
I wonder if emergent abilities are just the result of better clustering. For example, a clustering algorithm with rich datasets could improve the quality and number of clusters so to provide emergent abilities. As the number of parameters in the LLM is increased and the trained allows the embedding to construct a richer hypothesis space, the clustering algorithm could construct new centers that are the origin of the emergent capabilities. Also it could be related to the percolation threshold (1) (1) https://en.wikipedia.org/wiki/Percolation_threshold https://en.wikipedia.org/wiki/Percolation_threshold
Havoc 4y ago
I like that he actually attempted to define emergent behaviour - hadn't seen that before. That said it doesn't seem like a good definition. Bigger models can do more things than smaller models. That to me doesn't make the delta between them "emergent". Not that I've got a better definition...
- kerkeslager 4y ago
  The problem isn't that they should have defined "emergent" better, it's that they should have used a different word that actually means what they intend to communicate, such as "size-based" or "scale-dependent".
- bravura 4y ago
  To me, it seems like a good definition. If scaling the size of the model leads to a linear increase in capability, then nothing is emergent. If scaling the size of the model accretes almost no gains, and then there is a huge unpredicted step in capabilities, a capability has "emerged".
  - chasd00 4y ago
    I thought the thing that is scaring people is that there isn’t a defined relationship between model scale and capabilities. You may triple the model size and get no increase but then quadruple it and get 10,000% increase in capability. So no one knows where these massive models are headed and people want to pause until they understand the relationship.
  - layer8 4y ago
    Emergence doesn’t need to depend on scale. For example, gliders in the Game of Life are emergent behavior, and they don’t depend on scale. The way proteins fold is emergent from the physical forces, but is not a function of scale, just of particular configurations. Emergence is about the level on which an explanation or description works. You only “see” the glider move at a higher level of description than the cellular rules.
  - Havoc 4y ago
whacked_new 4y ago
I have a feeling that based on these emergent abilities, at some point, a sufficiently large multi-modal model will demonstrate a semblance of self consciousness (by way of answering all the designed queries correctly), and the world at large will hail this great achievement, and some hell breaks loose. And I suspect the semblance of self consciousness is actually a semblance. It will quack like self consciousness but is not actually self conscious. Just a feeling. But particularly after seeing how these phenomena are organized this way, I can't see how continual emergence of increasingly sophisticated rearrangement and reproduction of information will actually lead to consciousness.
- GuB-42 4y ago
  How do I know you are conscious? I know I am, by definition, but you? The only thing I know about you comes from what you are saying, and if I see you in person, that you are made of organic matter, and going deeper, that you have some sort of biological computer in your head. The reason I think you are as conscious as I am is because you act and look a lot like the only conscious being I am aware of: me. I am made of the same stuff as you are and we grew up in roughly the same way, I have the same kind of biological computer in my head, you express emotions a bit like I do, etc... In other words, I think you are self conscious because you quack like self consciousness. Quacking is all we have to determine what is conscious and what is not.
  - DennisP 4y ago
    But an AI doesn't qualify on some of those criteria.
    - lucubratory 4y ago
      But does it qualify on the ones that matter? I genuinely don't think it matters whether a person's mind is running on fat and water or silicon and copper. What should matter is if it's a person. We need a test for it that we're willing to stick to.
      - 4y ago
lynx23 4y ago
With all this consciousness discussion, and consciousness being a purely subjective thing. Please explain why you assume by default that every human actually has a "real consciousness"? How do you substantiate this assumption? And, given mental illnesses, where do you draw the line? Is everyone capable of speech conscious according to your idea of the world? What is the differenece between an LLM and a human stochastic parrott? How would you determine the difference? We are falling for the same error again and again. We consider humans something special, simply because we belong to that group, and would like us to be something special. But given the language barrier to other animals--roughly the same barrier we would have if we'd stumble over aliens--I really think we have pretty much no idea how other living things work and feel.
- highduc 4y ago
  We sure as hell avoid having a clear definition for what "consciousness" is. Nothing is good enough. Somehow there always seems to be something special in us that is impossible to replicate. Even with no evidence of it. What we perceive as consciousness MUST be replicable or else we're living in magicland and nobody can replicate that magic.

157 points by tlb 4y ago

They use the word "emergent" to mean: not present in small models but is present in large models I don't like that term, because it implies something deeper, so people immediately misinterpret this kind of information. (Just read this thread.)

Chirono 4y ago
What would you propose instead?
- jmull 4y ago
  Just "abilities present in large models not present in small models". This also makes clear its relative nature (that is, what "large" and "small" mean), which the use of the word "emergent" obscures.
  - 1123581321 4y ago
    That’s much longer and raises more questions (what is large and small, does this gradually or discretely appear.)
  - LawTalkingGuy 4y ago
    There's things which small models can't do, and where obviously a large model works better. Not emergence, I agree. I think they're using emergent for a different category of things - still things that a small model couldn't do, but specifically which a large model can just do without any of the work we thought we'd have to do for that ability. Or at least, that's how I see emergent being a useful term, both in a behavior and a value context.
bangkoksbest 4y ago
"Emergent" as used here is akin to how physicists use the word. "Emergent" as you interpret it (with the implication of something deeper) is more akin to how philosophers use the word. Almost every discussion I've read in philosophy over emergence is tedious and hard to see any use, whereas in physics it's intuitive and descriptively useful. My sense is that it's better to follow the physics style and stress there's no metaphysics implied.
- kerkeslager 4y ago
  > "Emergent" as used here is akin to how physicists use the word. "Emergent" as you interpret it (with the implication of something deeper) is more akin to how philosophers use the word. No, if you had read even the first paragraph of the article, you'd have seen that the person you're responding to isn't "interpreting" the article, they're quoting the article. Don't correct people when you don't know what you're talking about. You haven't read the article so you're in no position to talk about the article. This is the level of discussion I've come to expect from Hacker News, unfortunately.
  - bangkoksbest 4y ago
    What are you talking about? I read the article, there is literally no implied metaphysics about deeper emergence in the article at all.
- JanisErdmanis 4y ago
  I agree with physics notion of emergence used in the article. A single water molecule does not make water it’s properties of density, viscosity, specific heat and etc. There needs to be large number of molecules when such properties emerge.
- cowl 4y ago
  Where do you see this kind of Meaning in physics? In physics we have of example the Emergence of fluid dynamics and is clearly defined as a behaviour or property of system that does not derive simply from the sum of combination of it's subsystems. (it's not observed in small quantities of particles and it's not inherent to the particles) We have for example the Emergent behaviour of a school of fish Swimming in a new behaviour that is not observed on few fish etc. That is the mental model of "Emergence" most of us are familiar with. A new behaviour that can not be observed or "explained by" the sum of the parts of system. There is no metaphysics here. What is observed here is not new or different behaviour, it's simple a more precise and extensive version of the same behaviour. What is observed here is like comparing a 2Mpix resolution camera with a 12Mpix low light camera and saying that seeing more details in photo taken at 8pm is an emergent property of the camera because those details where not there in the previous version.
cwmoore 4y ago
The implication of something deeper is present in emerging features of the larger models. What is that if not emergent intelligence? Can you expand?

m3kw9 4y ago

Why is there a surprise when a larger model works better? And call it emergent?

typon 4y ago
There's no a priori reason to assume that models would see a step function improvement in abilities as their size increases. Perhaps the null hypothesis would be that we'd observe a linear increase in capability. Empirical evidence shows that's not the case, and that's why studying this phenomena is interesting.
- deleted 4y ago
  [deleted]
- majormajor 4y ago
  The judgement criteria itself looks step-like for many of these things. Word unscrambling, for instance: does a 15 percentage point increase in accuracy suggest that you're 15x better at it (from close to 0 to 15%ish)? Or could you do that by being modestly better, but that's just enough to cross the threshold to solve the simplest 15% of words in the task challenge you're being graded on? We're also using a log scale on the X axis there but a linear one for the Y axis which amplifies the "steppiness"
  - bob1029 4y ago
    This is a fair point. Things like arithmetic either work or they don't. Is partial credit issued as a linear interpolation between the predicted value and expected value? If not, then you necessarily will have step-like behavior in the data.
int_19h 4y ago
The surprise is that it's qualitatively better. Like being able to talk and follow instructions where smaller models couldn't do either at all.

lsy 4y ago

There is something about this that seems unscientific. What defines an "ability"? It sort of feels like the Guinness-Book type records for "most laps done around the Washington monument with a duck on your head". Who is making up these ability tests and why? As an example, I clicked through to "english russian proverbs" which gives the model an english input and four russian proverbs, and asks the model to pick the closest one in meaning. There are 80 of these questions. My question is "why this task? why 80? why 4 options?" The authors give some nonspecific reasoning in the github page, but it really feels like people are out there creating random tasks as they think of them, running them all against models, and the ones that do better get added to a list to make headlines like "137 emergent abilities of language models", when the number of abilities models can do seems dominated by the number of "abilities" that have been invented, rather than any property of the model. I suspect that if we continued to use the same process to create twice as many "abilities", we would see something like twice as many that models can do. The fact that the models get better at these constructed tasks when they have more parameters also seems like a no-brainer. You are adding coefficients to a function. A function with 1 coefficient will not be able to describe much. A function with 2 describes more. If you are memorizing the internet, having more coefficients means you can memorize more stuff, and even stuff with only a few examples can be better-fit in the billions of parameters. With growing training set size, and companies hiding their data, we also run into the issue of nobody really knowing what is in the training set. Did someone clone the big-bench repository and it got sucked into a training set and that's why the model does really well on its tasks after a certain number of parameters?

zamfi 4y ago
I can't speak to this particular paper, but the field of NLP is full of these kinds of tasks, it's in some sense a historical accident of where LLMs originated. These tasks are all noisy, all pretty messy, but the interesting part here is not that performance improves on these constructed tasks -- NLP researchers have been improving performance on constructed tasks for decades, by customizing models and curating datasets. Rather, the interesting part is that performance improves despite no specific model or data shifts except for scale. It seems that you're arguing two things: first, that these tasks aren't that interesting -- on that we agree. But, second, that it's obvious that performance would improve on arbitrary tasks because of memorization and an unknown training set -- but this is easy to validate: make up a new constructed task, and see how it performs.

jwie 4y ago

They have no other abilities in the first place. The adjectives are just as meaningless. "Ask" it about academic articles and their authors for {topic} and to summarize a body of research. The names might even be real people, but they won't have written the article, which probably doesn't exist. What it will do is sound like an article title, and to anyone unfamiliar with that body of work, will sound plausible. If your area of inquiry doesn't require valuable or reliable information as an output, sure I guess they have value. But for anything that's hard and matters, it's a parlor trick unfit for use.

Nevermark 4y ago
The models are trained to respond with “guesses” too confidently, due at at least three aspects of using online discussions as examples: 1. The obvious one: Lots of examples of humans with no clue, talking like they are absolute experts. 2. Humans without anything to contribute to discussions are not pressured to say something. They may be “there” in the discussion, but the model can’t learn from their silence. Unlike how we are forcing these uninformed outputs from the models. They need balanced examples of verbally expressed knowledge modesty to do this better. 3. Human’s implicit factoid omniscience, while they are online, given Google access, creates expectations that the non-Googling model can’t match.
- LawTalkingGuy 4y ago
  > They need balanced examples of verbally expressed knowledge modesty to do this better. This is generally pretty easy, with ChatGPT at least, in my experience. Langchain uses this prompt: "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know." Also, just don't ask it for things it couldn't possibly know without making it up like medical citations. This is more for where you want your "AI" assistant to say it does not know how to book a reservation rather than pretend that it can.
  - Nevermark 4y ago

pl90087 4y ago

[flagged]

gpderetta 4y ago
An LLM-based classifier would be great at this task! And you can ask ChatGPT to write the extension for you!
mecsred 4y ago
Maybe you should ask ChatGPT to write you one.

mikewarot 4y ago

It would be interesting to see if these abilities were to go away if you subjected the large model to drop-outs as you continued training, until it were reduced to the size of the small model. I think once an "ability" is learned by the model, it is useful to help compress information, and is more likely than not (>50%) to be retained.

amelius 4y ago

> In Emergent abilities of large language models, we defined an emergent ability as an ability that is “not present in small models but is present in large models.” Shouldn't it be defined as the ability of an LLM to do stuff that it was not explicitly trained to do (i.e. not a direct reproduction of what is in the training data)?

jschveibinz 4y ago

Some basic propositions to consider as I am forming my opinion on this: 1. A static (relative) set of things is in one state until something acts upon the set. 2. A thing can have memory, or characteristics. 3. A single thing can appear differently to an observer depending upon the perspective of the observer. 4. Emergence is the appearance of a collection of things that is dependent upon the action and or interaction of the set of things and their characteristics, any externals (the environment) AND the perspective of the observer. Am I missing anything in my framing of the question?

olodus 4y ago

Off topic but the concept of "emergent behavior" is just so cool. It is like modern adventure and exploration. No one really knows what things emerge when we run models with emergent behavior further than we have yet seen and together with other things it hasn't yet interacted with. We make guesses, further models, we gain deeper understanding...

svnt 4y ago

In what technical way are these properties emergent? It seems that stated another way they are simply a natural consequence of more accurate language prediction and therefore not emergent except in how we perceive them.

kerkeslager 4y ago
Because they defined the word "emergent" in a way that is misleading, likely to make their study seem more interesting. To quote the article: > [W]e defined an emergent ability as an ability that is “not present in small models but is present in large models.” It's not a bad study but using that word in that way is extremely poor communication, which has predictably been seized upon by every breathless nontechnical AI enthusiast.
hoseja 4y ago
Isn't that what emergence is?
Nevermark 4y ago
> … they are simply a natural consequence of more … That’s what emergent means. Something scales quantitatively, and after reaching certain levels, without any other special prompting, something else changes qualitatively. Presumably we recognize these things, then learn something after we have investigated and found out what the new mode of operation is, why it needed what it did to emerge, and possibly find new efficiencies or other ways to amplify the emergent effects.
nivenkos 4y ago
Because it's not inherent to the system itself. For example there's nothing inherent to token prediction to make it capable of doing arithmetic and you wouldn't expect a small/briefly trained model to manage it. But if learning a very large model on a huge dataset leads to it "learning" the decimal number system and arithmetic via token prediction then that is an emergent capability. It's just like the Chinese Room thought experiment - https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thought_experiment https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thou...

cultureswitch 4y ago

Love how "German" is an emergent property

didnotreadit 4y ago

I wonder if emergent abilities are just the result of better clustering. For example, a clustering algorithm with rich datasets could improve the quality and number of clusters so to provide emergent abilities. As the number of parameters in the LLM is increased and the trained allows the embedding to construct a richer hypothesis space, the clustering algorithm could construct new centers that are the origin of the emergent capabilities. Also it could be related to the percolation threshold (1) (1) https://en.wikipedia.org/wiki/Percolation_threshold https://en.wikipedia.org/wiki/Percolation_threshold

Havoc 4y ago

I like that he actually attempted to define emergent behaviour - hadn't seen that before. That said it doesn't seem like a good definition. Bigger models can do more things than smaller models. That to me doesn't make the delta between them "emergent". Not that I've got a better definition...

kerkeslager 4y ago
The problem isn't that they should have defined "emergent" better, it's that they should have used a different word that actually means what they intend to communicate, such as "size-based" or "scale-dependent".
bravura 4y ago
To me, it seems like a good definition. If scaling the size of the model leads to a linear increase in capability, then nothing is emergent. If scaling the size of the model accretes almost no gains, and then there is a huge unpredicted step in capabilities, a capability has "emerged".
- chasd00 4y ago
  I thought the thing that is scaring people is that there isn’t a defined relationship between model scale and capabilities. You may triple the model size and get no increase but then quadruple it and get 10,000% increase in capability. So no one knows where these massive models are headed and people want to pause until they understand the relationship.
- layer8 4y ago
  Emergence doesn’t need to depend on scale. For example, gliders in the Game of Life are emergent behavior, and they don’t depend on scale. The way proteins fold is emergent from the physical forces, but is not a function of scale, just of particular configurations. Emergence is about the level on which an explanation or description works. You only “see” the glider move at a higher level of description than the cellular rules.
- Havoc 4y ago

whacked_new 4y ago

I have a feeling that based on these emergent abilities, at some point, a sufficiently large multi-modal model will demonstrate a semblance of self consciousness (by way of answering all the designed queries correctly), and the world at large will hail this great achievement, and some hell breaks loose. And I suspect the semblance of self consciousness is actually a semblance. It will quack like self consciousness but is not actually self conscious. Just a feeling. But particularly after seeing how these phenomena are organized this way, I can't see how continual emergence of increasingly sophisticated rearrangement and reproduction of information will actually lead to consciousness.

GuB-42 4y ago
How do I know you are conscious? I know I am, by definition, but you? The only thing I know about you comes from what you are saying, and if I see you in person, that you are made of organic matter, and going deeper, that you have some sort of biological computer in your head. The reason I think you are as conscious as I am is because you act and look a lot like the only conscious being I am aware of: me. I am made of the same stuff as you are and we grew up in roughly the same way, I have the same kind of biological computer in my head, you express emotions a bit like I do, etc... In other words, I think you are self conscious because you quack like self consciousness. Quacking is all we have to determine what is conscious and what is not.
- DennisP 4y ago
  But an AI doesn't qualify on some of those criteria.
  - lucubratory 4y ago
    But does it qualify on the ones that matter? I genuinely don't think it matters whether a person's mind is running on fat and water or silicon and copper. What should matter is if it's a person. We need a test for it that we're willing to stick to.
    - 4y ago

lynx23 4y ago

With all this consciousness discussion, and consciousness being a purely subjective thing. Please explain why you assume by default that every human actually has a "real consciousness"? How do you substantiate this assumption? And, given mental illnesses, where do you draw the line? Is everyone capable of speech conscious according to your idea of the world? What is the differenece between an LLM and a human stochastic parrott? How would you determine the difference? We are falling for the same error again and again. We consider humans something special, simply because we belong to that group, and would like us to be something special. But given the language barrier to other animals--roughly the same barrier we would have if we'd stumble over aliens--I really think we have pretty much no idea how other living things work and feel.

highduc 4y ago
We sure as hell avoid having a clear definition for what "consciousness" is. Nothing is good enough. Somehow there always seems to be something special in us that is impossible to replicate. Even with no evidence of it. What we perceive as consciousness MUST be replicable or else we're living in magicland and nobody can replicate that magic.