On Training Data Influence of GPT Models (2024)

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 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 make our code and data publicly available at https://github.com/ernie-research/gptfluence.

## introduction the advent of generative language models, particularly the gpt series @xcite @xcite , has marked a paradigm shift in natural language processing (nlp) @xcite , code generation @xcite , visual and language understanding @xcite . these models have redefined performance standards across an extensive range of tasks, igniting detailed investigations into the process of training dynamics and the intricate nature of learned representations. despite these strides, the specific influence of individual current research has yet to focus comprehensively on the influence of training data on autoregressive language models. prior studies, such as those utilizing the bert @xcite or t5 architecture @xcite , have predominantly concentrated on natural language understanding tasks, leaving a considerable void in the exploration of generative language models. furthermore, the majority of this research @xcite @xcite @xcite has focused on test loss as the primary metric of interest, neglecting other vital performance indicators. metrics such as bleu @xcite and rouge @xcite scores are crucial for a thorough evaluation of a model's capabilities, particularly in the context of generative language models where downstream task performance is paramount. additionally, the challenge of generalizability-extending methodologies to accommodate unseen data-persists as a significant barrier @xcite . this is particularly critical for models expected to adapt to the dynamic and evolving trajectory of nlp tasks. in response to these gaps, we introduce gptfluence, a novel framework designed to extend the analysis of training data influence beyond the limitations of existing methodologies and across a broader spectrum of tasks. employing a featurized simulation approach, gptfluence estimates the impact of individual training examples on the performance of gpt models, covering both natural language understanding and generation tasks. this expanded focus facilitates a comprehensive understanding of model training dynamics, provid-ing insights into a wide array of evaluation metrics beyond mere test loss. extensive experiments on selected subsets from flan datasets @xcite , across a variety of tasks and gpt model variants @xcite , ranging in size from 14 million to 2.8 billion parameters, validate the effectiveness and superiority of our approach. notably, our method not only sheds light on the training dynamics of gpt models but also demonstrates remarkable generalization capabilities to unseen data. contribution to summarize, our contributions are as follows: ## preliminaries in this section, we revisit the conceptual framework of training data attribution (tda) methods, aiming to quantify the impact of individual training instances on the performance of models with respect to test data points. ## related work our methodology extends the frontier of tda techniques, which are instrumental in understanding the influence of individual training instances on model predictions. this body of work bifurcates into two main strands: gradient-based approximation methods and simulation-based approaches. ## conclusion and future work in this paper, we explore the data attribution analysis for gpt models through gptfluence, a novel featurized simulator approach. this methodology not only surpasses the predictive capabilities of traditional test loss metrics but forecasts essential task performance metrics across a broad spectrum of gpt model sizes, ranging from 14m to 2.8b parameters. our comprehensive evaluations across diverse downstream tasks and fine-tuning scenarios substantiate the superior efficacy of our approach. in the future, extending this approach to other tasks and training regime presents a promising avenue for future research.