To Think or Not to Think: The Hidden Cost of Meta-Training with Excessive CoT Examples

This article, sourced from ArXiv, likely explores the efficiency and potential drawbacks of using Chain-of-Thought (CoT) examples in meta-training Large Language Models (LLMs). It suggests that an overabundance of CoT examples might lead to hidden costs, possibly related to computational resources, overfitting, or a decline in generalization ability. The research likely investigates the optimal balance between the number of CoT examples and the performance of the LLM.