OFA tunes LayerNorm parameters as optimization preconditioners and adds step-ratio and sharpness penalties, reporting consistent few-shot accuracy gains over baselines on Llama and GPT-2 models.
Transformers as statisticians: Provable in-context learning with in-context algorithm selection
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Optimization-Inspired Few-Shot Adaptation for Large Language Models
OFA tunes LayerNorm parameters as optimization preconditioners and adds step-ratio and sharpness penalties, reporting consistent few-shot accuracy gains over baselines on Llama and GPT-2 models.