Quantitative Bounds for Length Generalization in Transformers
NeutralArtificial Intelligence
A recent study on length generalization in transformers sheds light on how these models can maintain performance when faced with longer sequences than they were trained on. While previous research indicated that transformers eventually achieve this capability after a certain training length, the exact threshold remains unclear. This work aims to clarify the necessary training sequence length for effective length generalization, which is crucial for improving the robustness of machine learning models in real-world applications.
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