On Training Data Influence of GPT Models

arXiv:2404.07840v1 Announce Type: cross
Abstract: Amidst the rapid advancements in generative language models, the investigation of how training data shapes the performance of GPT models is still emerging. This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models. Our approach not only traces the influence of individual training instances on performance trajectories, such as loss and other key metrics, on targeted test points but …

abstract arxiv cs.cl cs.lg data dynamics examples generative gpt gpt models impact influence investigation language language models novel paper performance simulation training training data type