The paper explores how transformers generalize from in-weights learning versus in-context learning, highlighting the distinction between rule-based and exemplar-based generalization. It investigates how the structure of language influences rule-based generalization in large language models.

The key takeaways for engineers/specialists from the paper are: 1. In-context learning in large language models tends to be rule-based, suggesting the influence of language structure. 2. Model size and training data structure play crucial roles in shaping the inductive biases of transformers. 3. Pretraining strategies can be used to induce rule-based generalization from context.

Read full paper: https://arxiv.org/abs/2210.05675

Tags: Artificial Intelligence, Deep Learning, Machine Learning

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