AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results show that a model's inductive generalization reflects the latent organization of its training data, with implications for data curation and for predicting how far a learned fact will spread.
This work investigates memorization in transformer-based language models by analyzing their memorization dynamics during training over multiple epochs and finds that memorization is neither a constant accumulation of sequences nor simply dictated by the recency of exposure to these sequences.
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