Evaluating Sample Utility for Data Selection by Mimicking Model Weights
Apple Machine Learning Research
Foundation models are trained on large-scale web-crawled datasets, which often contain noise, biases, and irrelevant information. This motivates the use of data selection techniques, which can be divided into model-free variants — relying on heuristic rules and downstream datasets — and model-based, e.g., using influence functions. The former can be expensive to design and risk introducing unwanted dependencies, while the latter are often computationally prohibitive. Instead, we propose an efficient, model-based approach using the Mimic Score, a new data quality metric that leverages the…
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25/02/2025 – 21:08 /
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