The study delves into how architecture influences pre-trained language models' base capabilities, particularly focusing on FFN-Wider Transformers. By analyzing the contribution ratio of combination functions, the research introduces CEA as a solution to enhance base capabilities. Experimental results demonstrate improvements in model performance with CEA implementation.
The content discusses the impact of architecture on language models' abilities beyond in-distribution language modeling, including out-of-distribution language modeling and transfer learning. It highlights the importance of understanding architecture's role in enhancing base capabilities and proposes practical solutions like CEA.
Key findings reveal that altering the width ratio of FFN layers can significantly affect model performance, with a decrease in transformation function contribution leading to a decline in base capabilities. The study extends its analysis to MoE Transformers, showcasing potential improvements through architectural enhancements.
Overall, the research provides valuable insights into optimizing architecture for enhanced base capabilities in pre-trained language models, offering guidance for future improvements and design considerations.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Xin Lu,Yanya... at arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.02436.pdfDeeper Inquiries