Predicting and Interpreting In-Context Learning Curves Through Bayesian Scaling Laws

Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical challenge in this field is understanding and predicting the relationship between the number of demonstrations provided and the model’s performance improvement, known as the ICL curve. This relationship needs to be […]

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