Summary CABRITA Closing the Gap for Foreign Languages arxiv.org
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One Line
Cabrita is a methodology that enhances foreign language pre-trained models through the use of a more efficient tokenizer.
Slides
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Key Points
- Cabrita is a methodology that addresses the limitations of pre-trained models in foreign languages by introducing a new tokenizer.
- Adapting a Large Language Model to a new language presents challenges with the tokenizer behavior.
- The study utilized a TPU v3-8 for training and performed 128 accumulation steps to achieve the target batch size.
- The Cabrita approach offers comparable performance to conventional continued pre-training and enhanced inference efficiency.
- openCabrita3B consistently outperforms GPT-J in terms of performance.
- Employing larger-scale models could yield promising results for foreign language processing.
- The document discusses various language models and tokenizers used for foreign languages, particularly focusing on Portuguese.
Summaries
17 word summary
Cabrita is a methodology that improves pre-trained models for foreign languages by introducing a more efficient tokenizer.
43 word summary
Cabrita is a methodology that aims to address the limitations of pre-trained models in foreign languages by introducing a new tokenizer. The default tokenizer for the Portuguese language in the OpenLLaMA model is overly verbose, resulting in the division of text into small
236 word summary
Cabrita is a methodology that aims to address the limitations of pre-trained models in foreign languages. The main challenge is the high cost associated with training models from scratch. To overcome this, Cabrita relies on available pre-trained models but introduces a new tokenizer
Adapting a Large Language Model to a new language presents challenges with the tokenizer behavior. The default tokenizer for the Portuguese language in the OpenLLaMA model is overly verbose for non-English examples, resulting in the division of text into small parts
The study utilized a TPU v3-8 for training, with batches of 16 containing a sequence of 2048 tokens. 128 accumulation steps were performed to achieve the target of 2048 samples in a batch, resulting in a throughput
The Cabrita approach, which involves adapting the tokenizer, offers a comparable performance level to conventional continued pre-training, with the added benefit of enhanced inference efficiency. The performance of openCabrita3B is satisfactory, consistently outperforming GPT-J and
The authors of the document express their conviction that employing larger-scale models could yield promising results for foreign language processing. They mention the successful experiment with Chinese language models as a basis for this line of thinking. However, they note that the absence of a structured
The document discusses various language models and tokenizers used for foreign languages, particularly focusing on Portuguese. It mentions models such as GPT-2, MPT Falcon, OpenLLaMA, and BERTau, along with their respective vocab sizes and