In the ever-evolving landscape of artificial intelligence, the quest for high-quality training data remains a paramount challenge. Researchers are constantly seeking innovative methods to evaluate and select data that maximizes model performance. A recent research paper explores the fascinating connection between "in-context probing" (ICP) and "influence functions," unveiling a potential breakthrough in data valuation. Imagine being able to assess the value of a single piece of training data simply by prompting a large language model (LLM). That's the power of in-context probing. This technique leverages the inherent knowledge within LLMs to determine the quality and relevance of training examples. But how does it actually work? The research reveals a surprising link between ICP and influence functions, a mathematical tool used to estimate the impact of individual data points on model predictions. The paper suggests that ICP acts as a cost-effective proxy for these computationally intensive influence functions. This discovery has significant implications for data selection, particularly in fine-tuning scenarios. By using ICP, researchers can efficiently identify high-value training samples, potentially leading to smaller, more efficient datasets that still yield impressive model performance. The empirical findings demonstrate a strong correlation between data rankings generated by ICP and influence functions, suggesting a deeper underlying connection. Fine-tuning experiments further reinforce this link, showcasing similar model performance when trained on data selected by either method. While this research focuses on instruction-following tasks, it opens doors to explore ICP's potential across various AI applications. Further investigation into different training stages and the selection of data groups could reveal even more powerful insights. This innovative approach promises to streamline data valuation, making the development of high-performing AI models more efficient and accessible.