What are the challenges and solutions for data tokenization in multi-lingual generative models

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Can you name the challenges and solutions for data tokenization in multi-lingual generative models?
Nov 20 in Generative AI by Ashutosh
• 5,810 points
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Challenges and solutions for data tokenization in multi-lingual generative models are as follows:

Challenges in Multi-lingual Tokenization:

  • Vocabulary Size: Handling large vocabularies for diverse languages leads to memory and efficiency issues.
  • Rare Tokens: Languages with fewer training examples produce many out-of-vocabulary (OOV) tokens.
  • Script Variability: Different scripts (e.g., Latin vs. Cyrillic) require flexible tokenization strategies.
  • Consistency: Tokenization inconsistencies across languages impact model performance.

Solutions for that:

  • Subword Tokenization: It uses algorithms like Byte Pair Encoding (BPE) or SentencePiece to generate subword units shared across languages.
  • Shared Vocabulary: Train a common vocabulary to leverage cross-lingual transfer.
  • Language Tags: It Adds language-specific tokens (e.g., <en> for English) to guide the model.

The outcome of the above code would be that subword tokenization handles OOV words efficiently, and shared vocabulary supports cross-lingual understanding.

answered Nov 21 by Ashutosh
• 5,810 points

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