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This research introduces DA-Cramming, a novel pretraining framework that integrates dependency agreement information to significantly reduce the computational costs of training foundational language models. By transforming these structural agreements into embeddings during the pretraining phase rather than relying solely on fine-tuning, the method achieves superior performance across various tasks while maintaining efficiency. This advancement directly addresses the critical challenge of making high-quality model development more accessible and cost-effective for researchers with limited hardware resources.
DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration
arXiv:2311.04799v3 Announce Type: replace-cross Abstract: Pretraining language models is still a challenge for many researchers due to its substantial computational costs. As such, there is growing interest in developing more affordable pretraining methods. One notable advancement in this area is the Cramming technique (Geiping and Goldstein, 2022), which enables the pretraining of BERT-style language models using just one GPU in a single day. Building on this innovative approach, we introduce the Dependency Agreement Cramming (DA-Cramming), an efficient framework that integrates information about dependency agreements into the pretraining process. Unlike existing methods that leverage similar semantic information during finetuning, our approach represents a pioneering effort focusing on enhancing the foundational language understanding with semantic information during pretraining. We meticulously design a dual-stage pretraining work flow with four dedicated submodels to capture representative dependency agreements at the chunk level, effectively transforming these agreements into embeddings to benefit the pretraining. Extensive empirical results demonstrate that our method significantly outperforms previous methods across various tasks.
Abstract:Pretraining language models is still a challenge for many researchers due to its substantial computational costs. As such, there is growing interest in developing more affordable pretraining methods. One notable advancement in this area is the Cramming technique (Geiping and Goldstein, 2022), which enables the pretraining of BERT-style language models using just one GPU in a single day. Building on this innovative approach, we introduce the Dependency Agreement Cramming (DA-Cramming), an efficient framework that integrates information about dependency agreements into the pretraining process. Unlike existing methods that leverage similar semantic information during finetuning, our approach represents a pioneering effort focusing on enhancing the foundational language understanding with semantic information during pretraining. We meticulously design a dual-stage pretraining work flow with four dedicated submodels to capture representative dependency agreements at the chunk level, effectively transforming these agreements into embeddings to benefit the pretraining. Extensive empirical results demonstrate that our method significantly outperforms previous methods across various tasks.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2311.04799 [cs.CL]
(or arXiv:2311.04799v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2311.04799 Focus to learn more arXiv-issued DOI via DataCite
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