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Cornell University
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Low diversity in training data can lead to substantial performance drops in language models, revealing a critical oversight in data augmentation practices.
Forget runtime inference costs: "compiled AI" slashes token consumption by 57x while maintaining accuracy by pre-generating deterministic code from LLMs.
