Emergence and Scaling Laws
You will often hear that as models grow larger and are trained on more data, they not only improve at tasks they were already doing, but also begin to display entirely new abilities. This phenomenon is at the heart of scaling laws — empirical and theoretical relationships that describe how a model's performance changes as you increase its size (number of parameters), the amount of data it sees, or the compute used during training. In practice, researchers have observed that as you scale up these factors, models do not simply become incrementally better; instead, they sometimes exhibit sudden, qualitative jumps in capability. These jumps are called emergent abilities.
Scaling laws provide a way to predict how much improvement you might expect by increasing model size or dataset size. For instance, doubling the number of parameters might yield a predictable drop in error rate, but at certain scales, the model might suddenly master a new skill it previously could not — such as understanding jokes, following complex instructions, or performing arithmetic.
Empirical studies have shown that, up to a point, the relationship between model scale and performance follows a smooth curve, often a power law. However, at certain thresholds, new behaviors seem to "emerge" almost as if the model underwent a phase transition — a sudden change in state, similar to how water turns to ice at 0°C. These emergent abilities are not just better performance, but qualitatively different behaviors that were not present in smaller models.
A basic form of a scaling law can be written as:
L=aN−α+bwhere L is the loss (error), N is the number of model parameters (or data size), a and b are constants, and α is the scaling exponent. This equation predicts that as you increase N, the loss L decreases smoothly.
Emergent abilities do not arise gradually, but often appear abruptly once a model crosses a certain scale. This is analogous to a phase transition in physics, where a small change in temperature can suddenly turn water into ice. In models, once a critical threshold is passed, new qualitative behaviors — such as zero-shot reasoning or compositional generalization — can suddenly emerge.
Emergence is not simply more of the same. It refers to the appearance of qualitatively new behaviors that were not present, even in a weaker form, in smaller or less-trained models.
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Can you give examples of emergent abilities in large language models?
How do researchers identify when a new ability has emerged?
Are there any limitations or risks associated with scaling models?
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You will often hear that as models grow larger and are trained on more data, they not only improve at tasks they were already doing, but also begin to display entirely new abilities. This phenomenon is at the heart of scaling laws — empirical and theoretical relationships that describe how a model's performance changes as you increase its size (number of parameters), the amount of data it sees, or the compute used during training. In practice, researchers have observed that as you scale up these factors, models do not simply become incrementally better; instead, they sometimes exhibit sudden, qualitative jumps in capability. These jumps are called emergent abilities.
Scaling laws provide a way to predict how much improvement you might expect by increasing model size or dataset size. For instance, doubling the number of parameters might yield a predictable drop in error rate, but at certain scales, the model might suddenly master a new skill it previously could not — such as understanding jokes, following complex instructions, or performing arithmetic.
Empirical studies have shown that, up to a point, the relationship between model scale and performance follows a smooth curve, often a power law. However, at certain thresholds, new behaviors seem to "emerge" almost as if the model underwent a phase transition — a sudden change in state, similar to how water turns to ice at 0°C. These emergent abilities are not just better performance, but qualitatively different behaviors that were not present in smaller models.
A basic form of a scaling law can be written as:
L=aN−α+bwhere L is the loss (error), N is the number of model parameters (or data size), a and b are constants, and α is the scaling exponent. This equation predicts that as you increase N, the loss L decreases smoothly.
Emergent abilities do not arise gradually, but often appear abruptly once a model crosses a certain scale. This is analogous to a phase transition in physics, where a small change in temperature can suddenly turn water into ice. In models, once a critical threshold is passed, new qualitative behaviors — such as zero-shot reasoning or compositional generalization — can suddenly emerge.
Emergence is not simply more of the same. It refers to the appearance of qualitatively new behaviors that were not present, even in a weaker form, in smaller or less-trained models.
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