Summary of Dept: Decoupled Embeddings For Pre-training Language Models, by Alex Iacob et al.
DEPT: Decoupled Embeddings for Pre-training Language Models
by Alex Iacob, Lorenzo Sani, Meghdad Kurmanji, William F. Shen, Xinchi Qiu, Dongqi Cai, Yan Gao, Nicholas D. Lane
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel framework for language model pre-training that alleviates the “curse of multilinguality” caused by training on diverse and heterogeneous data sources. The proposed method, called DEPT, decouples embeddings from the transformer body while simultaneously training it in multiple contexts. This approach enables training robustly and effectively under significant data heterogeneity, reduces token embedding parameters by up to 80%, and enhances model generalization and plasticity in adapting to new languages and domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models learn better from different kinds of texts. Right now, this can be hard because the models are trained on many different types of text that have different words, grammar, and meanings. The researchers created a new way to train these models called DEPT. It lets them learn without needing all the same words across different languages and domains. This makes it possible for the models to learn faster, use less memory, and work better with new texts. |
Keywords
» Artificial intelligence » Embedding » Generalization » Language model » Token » Transformer
