5 months ago

Not all layers are equally as important: Every Layer Counts BERT

Lucas Georges Gabriel Charpentier; David Samuel

Abstract

This paper introduces a novel modification of the transformer architecture, tailored for the data-efficient pretraining of language models. This aspect is evaluated by participating in the BabyLM challenge, where our solution won both the strict and strict-small tracks. Our approach allows each transformer layer to select which outputs of previous layers to process. The empirical results verify the potential of this simple modification and show that not all layers are equally as important.

Benchmarks

Benchmark	Methodology	Metrics
linguistic-acceptability-on-cola	LTG-BERT-base 98M	Accuracy: 82.7
linguistic-acceptability-on-cola	ELC-BERT-base 98M	Accuracy: 82.6
linguistic-acceptability-on-cola	LTG-BERT-small 24M	Accuracy: 77.6
linguistic-acceptability-on-cola	ELC-BERT-small 24M	Accuracy: 76.1
natural-language-inference-on-multinli	ELC-BERT-base 98M (zero init)	Matched: 84.4 Mismatched: 84.5
natural-language-inference-on-multinli	ELC-BERT-small 24M	Matched: 79.2 Mismatched: 79.9
natural-language-inference-on-multinli	LTG-BERT-small 24M	Matched: 78 Mismatched: 78.8
natural-language-inference-on-multinli	LTG-BERT-base 98M	Matched: 83 Mismatched: 83.4
natural-language-inference-on-rte	LTG-BERT-small 24M	Accuracy: 53.7
natural-language-inference-on-rte	ELC-BERT-small 24M	Accuracy: 55.4
natural-language-inference-on-rte	ELC-BERT-base 98M (zero init)	Accuracy: 63
natural-language-inference-on-rte	LTG-BERT-base 98M	Accuracy: 54.7

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