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Google Brain’s Vec2Text Models for Sentence Generation Excel in Universality, Diversity, Fluency & Semantic Structure
In the new paper Vec2text With Round-Trip Translations, Google Brain researchers explore large language models’ capabilities for generating arbitrary natural language text from inputs of fixed-size vectors — a vec2text setting — and propose a simple data augmentation approach based on round-trip translations to improve vec2text model performance.
The post Google Brain’s Vec2Text Models for Sentence Generation Excel in Universality, Diversity, Fluency & Semantic Structure first appeared on Synced.
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