Generalization Patterns of Transformers in In-Weights Learning and In-Context Learning
Artificial Intelligence
Deep Learning
Machine Learning
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.
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About this paper
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.