What are the main challenges of tokenization in AI?

What are the main challenges of tokenization in AI?

Tokenization in AI faces several challenges, including handling ambiguity, context preservation, and language diversity.

Can you explain how ambiguity affects tokenization?

Ambiguity arises when a word has multiple meanings. Tokenization must accurately capture the intended meaning based on context.

What about context preservation? Why is it important?

Context preservation is crucial because it ensures that the meaning of tokens is understood in relation to surrounding words, improving comprehension.

Are there specific languages that are more challenging for tokenization?

Yes, languages with rich morphology or no clear word boundaries, like Chinese or agglutinative languages, pose significant tokenization challenges.

How do advancements in AI help overcome these challenges?

Advancements like deep learning and transformer models improve context understanding and ambiguity resolution, enhancing tokenization accuracy.