ESsEN is a parameter-efficient two-tower vision-language transformer that matches larger models on discriminative tasks after training end-to-end with limited data and resources.
American journal of speech-language pathology , volume=
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Injecting 1% synthetic data targeting specific constructions during pre-training of GPT-2 Small boosts performance on 8 of 9 weakest BLiMP paradigms (e.g., only_npi_scope from 20.9% to 69.4%), while aggregate performance holds or improves, with one resistant case.
citing papers explorer
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ESsEN: Training Compact Discriminative Vision-Language Transformers in a Low-Resource Setting
ESsEN is a parameter-efficient two-tower vision-language transformer that matches larger models on discriminative tasks after training end-to-end with limited data and resources.
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Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?
Injecting 1% synthetic data targeting specific constructions during pre-training of GPT-2 Small boosts performance on 8 of 9 weakest BLiMP paradigms (e.g., only_npi_scope from 20.9% to 69.4%), while aggregate performance holds or improves, with one resistant case.