Shall Your Data Strategy Work? Perform a Swift Study
This work addresses the need for data scientists and AI researchers to quickly assess data strategies, though it is incremental as it builds on existing gradient-based influence estimation methods.
The authors tackled the problem of efficiently evaluating instruction-tuning data strategies without retraining models, by proposing a gradient-based method that uses probe examples to predict data efficacy, and validated it by showing alignment with actual training outcomes.
This work presents a swift method to assess the efficacy of particular types of instruction-tuning data, utilizing just a handful of probe examples and eliminating the need for model retraining. This method employs the idea of gradient-based data influence estimation, analyzing the gradient projections of probe examples from the chosen strategy onto evaluation examples to assess its advantages. Building upon this method, we conducted three swift studies to investigate the potential of Chain-of-thought (CoT) data, query clarification data, and response evaluation data in enhancing model generalization. Subsequently, we embarked on a validation study to corroborate the findings of these swift studies. In this validation study, we developed training datasets tailored to each studied strategy and compared model performance with and without the use of these datasets. The results of the validation study aligned with the findings of the swift studies, validating the efficacy of our proposed method.