Globally Consistent Coloring Schemes for Language Identification
We study how little extra information is needed to make adversarial language learning possible. In Gold's model of language identification in the limit, a learner is given an enumeration of the strings from an unknown language chosen from a countable language collection. The learner guesses the identity of the language over the course of the enumeration, and it succeeds if, eventually, all of its guesses are the correct language. Classical results of Gold and Angluin show that many natural collections cannot be learned in this way. Recent work on trace colorings, motivated by the success of th
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- PossiblePossibly related (embedding) · 47%Knowledge Distillation of Black-Box Large Language Models →
- PossiblePossibly related (embedding) · 46%Knowledge Distillation of Black-Box Large Language Models (2024) →
- LinkedLinked via arxiv author · 85%Moses Charikar →
“Globally Consistent Coloring Schemes for Language Identification”
- LinkedLinked via arxiv author · 85%Jon Kleinberg →
“Globally Consistent Coloring Schemes for Language Identification”
- LinkedLinked via arxiv author · 85%Chirag Pabbaraju →
“Globally Consistent Coloring Schemes for Language Identification”