Summary of Training Bilingual Lms with Data Constraints in the Targeted Language, by Skyler Seto et al.
Training Bilingual LMs with Data Constraints in the Targeted Language
by Skyler Seto, Maartje ter Hoeve, Richard He Bai, Natalie Schluter, David Grangier
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper explores how to improve the performance of large language models when they are trained on insufficient data for a specific target language. The authors investigate the idea of leveraging data from an auxiliary language with abundant high-quality training data to boost model performance in the target language. They quantify the performance gap between training with data from the auxiliary language and the target language, and examine the benefits of using translation systems and scaling models when data is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about finding ways to make large language models better by using extra information from other languages where there’s more data. The authors want to know if it’s possible to use that extra data to improve performance in a target language with less data. They’re looking for ways to bridge the gap between what we can do with lots of training data and what we can do with limited data. |
Keywords
» Artificial intelligence » Translation
