Large language models (LLMs) like ChatGPT are trained on massive amounts of data. But how much does each piece of data actually contribute to the model's knowledge? Researchers have tackled this "data valuation" problem using a technique called influence functions. However, applying this technique to LLMs like ChatGPT has been computationally expensive. A new research paper introduces "LOGRA," an efficient way to calculate the influence of individual data points on massive models. The key innovation is projecting the high-dimensional gradients (think of them as directions of change during training) onto a smaller, more manageable space. This drastically reduces the computational burden, making data valuation feasible for large models and datasets. The researchers also built LOGIX, a software package that simplifies the process of transforming existing training code into data valuation code. In experiments, LOGRA showed competitive accuracy compared to more resource-intensive methods while being up to 6,500 times faster and using 5 times less memory. When applied to an 8-billion parameter LLM, the most valuable data identified by LOGRA often shared qualitative similarities with the model's output, suggesting the method is effectively pinpointing influential training examples. This research opens doors to fairly compensating data providers and understanding how LLMs learn. However, challenges remain, such as handling outlier data points and further scaling the system for even larger datasets. The future of data valuation looks bright, promising a more equitable and transparent relationship between data providers and the models that learn from their contributions.