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Alternate view: Are Emergent Abilities of Large Language Models a Mirage? https://arxiv.org/abs/2304.15004 https://arxiv.org/abs/2304.15004 "Here, we present

Alternate view: Are Emergent Abilities of Large Language Models a Mirage? https://arxiv.org/abs/2304.15004 https://arxiv.org/abs/2304.15004 "Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous predictable changes in model performance."

moffkalast 1y ago
That has been a problem with most LLM benchmarks. Any test that's rated in percentages tends to be logarithmic, since getting from say 90% to 95% is not a linear 5% improvement but probably more like a 2x or 10x improvement in practical terms, since the metric is already nearly maxed out and only the extreme edge cases remain that are much harder to master.
K0balt 1y ago
A decent thought-proxy for this : powered flight. An aircraft can approach powered flight without achieving it. With a given amount of thrust or aerodynamic characteristics, the aircraft will weigh dynamic_weight=(static_weight - x) where x is a combination of the aerodynamic characteristics and the amount of thrust applied. In no case where dynamic_weight>0 will the aircraft fly, even though it exhibits characteristics of flight, I.e the transfer of aerodynamic forces to counteract gravity. So while it progressively exhibits characteristics of flight, it is not capable of any kind of flight at all until the critical point of dynamic_weight<0. So the enabling characteristics are not “emergent”, but the behavior is. I think this boils down to a matter of semantics.
- scopemouthwash 1y ago
  “Thought-proxy”? I think the word you’re looking for is “analogy”.
  - K0balt 1y ago
    Analogy is a great word proxy for thought proxy. Yeah, I’m pretty sure analogy would have been fine there, I think maybe it fell off the edge of my vocabulary for a moment? Not really sure, but I really can’t think of any reason why “thought proxy” would have been more descriptive, informative, or accurate ¯_(ツ)_/¯
foobarqux 1y ago
The author himself explicitly acknowledges the paper but the incomprehensibly ignores it ("Even so, many would like to understand, predict, and even facilitate the emergence of these capabilities."). It's like saying "some say [foo] doesn't exist but even so many would like to understand [foo]". It's incoherent.
- autoexec 1y ago
  No point in letting facts get in the way of an entire article I guess.
jebarker 1y ago
Yes, this paper is under-appreciated. The point is that we as humans decide what constitutes a given task we're going to set as a bar and it turns out that statistical pattern matching can solve many of those tasks to a reasonable level (we also get to define "reasonable") when there's sufficient scale of parameters and data, but that tip-over point is entirely arbitrary.
Al-Khwarizmi 1y ago
The continuous metrics the paper uses are largely irrelevant in practice, though. The sudden changes appear when you use metrics people actually care about. To me the paper is overhyped. Knowing how neural networks work, it's clear that there are going to be underlying properties that vary smoothly. This doesn't preclude the existence of emergent abilities.

moffkalast 1y ago

That has been a problem with most LLM benchmarks. Any test that's rated in percentages tends to be logarithmic, since getting from say 90% to 95% is not a linear 5% improvement but probably more like a 2x or 10x improvement in practical terms, since the metric is already nearly maxed out and only the extreme edge cases remain that are much harder to master.

K0balt 1y ago

A decent thought-proxy for this : powered flight. An aircraft can approach powered flight without achieving it. With a given amount of thrust or aerodynamic characteristics, the aircraft will weigh dynamic_weight=(static_weight - x) where x is a combination of the aerodynamic characteristics and the amount of thrust applied. In no case where dynamic_weight>0 will the aircraft fly, even though it exhibits characteristics of flight, I.e the transfer of aerodynamic forces to counteract gravity. So while it progressively exhibits characteristics of flight, it is not capable of any kind of flight at all until the critical point of dynamic_weight<0. So the enabling characteristics are not “emergent”, but the behavior is. I think this boils down to a matter of semantics.

scopemouthwash 1y ago
“Thought-proxy”? I think the word you’re looking for is “analogy”.
- K0balt 1y ago
  Analogy is a great word proxy for thought proxy. Yeah, I’m pretty sure analogy would have been fine there, I think maybe it fell off the edge of my vocabulary for a moment? Not really sure, but I really can’t think of any reason why “thought proxy” would have been more descriptive, informative, or accurate ¯_(ツ)_/¯

foobarqux 1y ago

The author himself explicitly acknowledges the paper but the incomprehensibly ignores it ("Even so, many would like to understand, predict, and even facilitate the emergence of these capabilities."). It's like saying "some say [foo] doesn't exist but even so many would like to understand [foo]". It's incoherent.

autoexec 1y ago
No point in letting facts get in the way of an entire article I guess.

jebarker 1y ago

Yes, this paper is under-appreciated. The point is that we as humans decide what constitutes a given task we're going to set as a bar and it turns out that statistical pattern matching can solve many of those tasks to a reasonable level (we also get to define "reasonable") when there's sufficient scale of parameters and data, but that tip-over point is entirely arbitrary.

Al-Khwarizmi 1y ago

The continuous metrics the paper uses are largely irrelevant in practice, though. The sudden changes appear when you use metrics people actually care about. To me the paper is overhyped. Knowing how neural networks work, it's clear that there are going to be underlying properties that vary smoothly. This doesn't preclude the existence of emergent abilities.