Neural Networks for Learnable and Scalable Influence Estimation of Instruction Fine-Tuning Data
This work addresses scalability issues in influence estimation for large language models, enabling more efficient data analysis in machine learning, though it is incremental as it builds on existing influence function methods.
The paper tackles the high computational cost and poor generalization of influence estimation methods for language models by proposing a small neural network (InfluenceNetwork) that reduces costs by up to 99% and uses models 0.0027% the size of full models, with no performance compromise in subset selection for instruction fine-tuning.
Influence functions provide crucial insights into model training, but existing methods suffer from large computational costs and limited generalization. Particularly, recent works have proposed various metrics and algorithms to calculate the influence of data using language models, which do not scale well with large models and datasets. This is because of the expensive forward and backward passes required for computation, substantial memory requirements to store large models, and poor generalization of influence estimates to new data. In this paper, we explore the use of small neural networks -- which we refer to as the InfluenceNetwork -- to estimate influence values, achieving up to 99% cost reduction. Our evaluation demonstrates that influence values can be estimated with models just 0.0027% the size of full language models (we use 7B and 8B versions). We apply our algorithm of estimating influence values (called NN-CIFT: Neural Networks for effiCient Instruction Fine-Tuning) to the downstream task of subset selection for general instruction fine-tuning. In our study, we include four state-of-the-art influence functions and show no compromise in performance, despite large speedups, between NN-CIFT and the original influence functions. We provide an in-depth hyperparameter analyses of NN-CIFT. The code for our method can be found here: https://github.com/agarwalishika/NN-CIFT.