MT5 Large Language Model (LLM)
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An MT5 Large Language Model (LLM) is a multilingual LLM that is variant of the T5 (Text-to-Text Transfer Transformer) model, pre-trained on the mC4 corpus covering 101 languages and designed for a wide range of natural language processing tasks across multiple languages``【oaicite:6】````【oaicite:5】``.
- Context:
- It can (typically) handle tasks in languages that are often underrepresented in NLP research due to its extensive language coverage.
- It can (typically) use a pre-training method known as "span-corruption", where parts of the input text are masked and the model is trained to predict these masked tokens.
- It can (often) be available in various sizes, with models ranging from 300 million to 13 billion parameters, allowing flexibility in deployment.
- It can demonstrate state-of-the-art performance on a variety of multilingual benchmarks, including tasks like XNLI entailment, reading comprehension, Named Entity Recognition (NER), and paraphrase identification&
- It can (often) effectively balance the training between overfitting on low-resource languages and underfitting on high-resource languages, which is crucial for multilingual models.
- It can perform better in limited data scenarios, such as the few-shot setting, compared to its larger counterparts, making it effective in situations with scarce training data.
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- Example(s):
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- Counter-Example(s):
- See: Multilingual NLP, Pre-trained Language Models, Natural Language Processing, Span-Corruption Pre-training.