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 also enables a comprehensive comparison with existing methods across various training scenarios in GPT models, ranging from 14 million to 2.8 billion parameters, across a range of downstream tasks. Contrary to earlier methods that struggle with generalization to new data, GPTfluence introduces a parameterized simulation of training dynamics, demonstrating robust generalization capabilities to unseen training data. This adaptability is evident across both fine-tuning and instruction-tuning scenarios, spanning tasks in natural language understanding and generation. We will make our code and data publicly available.

翻译：在生成式语言模型快速发展的背景下，训练数据如何塑造GPT模型性能的研究仍处于起步阶段。本文提出GPTfluence，一种利用特征化模拟来评估训练样例对GPT模型训练动态影响的新方法。该方法不仅能追踪单个训练实例对目标测试点性能轨迹（如损失值及其他关键指标）的影响，还能够在从1400万至28亿参数的GPT模型变体中，针对多种下游任务全面对比现有方法。与早期方法难以泛化至新数据不同，GPTfluence引入了一种参数化的训练动态模拟，展现出对未见训练数据的强大泛化能力。这种适应性在微调与指令微调场景中均得到验证，涵盖自然语言理解与生成任务。我们将公开代码与数据。