Papers by Hyung Chung

2 papers
Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? (2023.findings-emnlp)

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Challenge: Existing studies on the scaling properties of model architectures have not explored the impact of inductive biases on scaling behaviour.
Approach: They conduct extensive experiments to understand scaling behaviour of ten different model architectures.
Outcome: The results show that the best performing model can fluctuate at different scales.
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)

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Challenge: Existing scaling of language models is expensive and requires significant computational costs.
Approach: They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute.
Outcome: The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute.

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