LLMs often compute correct answers to simple deterministic tasks in their internal representations, and light fine-tuning of early layers lets them access that information by suppressing a learned prior over outputs.
All experiments are trained for 50 epochs, use learning-rate = 10−4, weight-decay = 0.01, 14 Preprint
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Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models
LLMs often compute correct answers to simple deterministic tasks in their internal representations, and light fine-tuning of early layers lets them access that information by suppressing a learned prior over outputs.