Model behavior is determined by the dataset
Overview
Section titled “Overview”The final behavior of a model is entirely determined by its training dataset, not by the model architecture, hyperparameters, or optimizer.
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Models are "high-precision replicas" of the dataset: During training, models not only learn the explicit knowledge within the dataset (like what a cat is) but also grasp the extremely subtle, often imperceptible, underlying statistical patterns in the data distribution (such as human photo-taking preferences and word usage habits).
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Different architectures converge to the same point: Given the same dataset and sufficient training, different model architectures (like diffusion models, ViTs, etc.) ultimately converge to the same point, producing nearly identical results.
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Architecture and techniques are merely "means": All technical choices—model architecture, hyperparameters, optimizers—essentially serve as tools or methods to utilize computational power more efficiently, helping the model "approximate" and "fit" that one and only dataset.
When we refer to famous AI models like ChatGPT, Bard, or Claude, what we are essentially pointing to is not their model weights or technical architecture, but the unique dataset behind them. The name of a model is, in fact, a proxy for its dataset.