Shall Your Data Strategy Work? Perform a Swift Study

TL;DR

This paper introduces a rapid, gradient-based method to evaluate the effectiveness of instruction-tuning data strategies without retraining models, validated through multiple studies on different data types.

Contribution

The paper presents a novel swift assessment technique for instruction-tuning data effectiveness, validated by experiments on various data strategies and a subsequent validation study.

Findings

Gradient-based influence estimation effectively predicts data strategy benefits.

Chain-of-thought, query clarification, and response evaluation data improve model generalization.

Validation confirms the swift method's accuracy in assessing data strategy efficacy.

Abstract

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.