I will discuss DataInf, an efficient influence approximation method that is practical for large-scale generative AI models. Our theoretical analysis shows that DataInf is particularly well-suited for parameter-efficient fine-tuning techniques such as LoRA. In applications to generative models such as Llama-2 and stable-diffusion, DataInf effectively identifies the most influential fine-tuning examples and is substantially faster than previous methods. Moreover, it can help to identify which data points are mislabeled.