Data Selection for Fine-tuning Large Language Models Using Transferred
  Shapley Values
Stephanie Schoch Ritwick Mishra Yangfeng Ji
Although Shapley values have been shown to be highly effective for
identifying harmful training instances, dataset size and model complexity
constraints limit the ability to apply Shapley-based data valuation to
fine-tuning large pre-trained language models. To address this, we propose
TS-DShapley, an algorithm that reduces computational cost of Shapley-based data
valuation through: 1) an efficient sampling-based method that aggregates
Shapley values computed from subsets for valuation of the entire training set,
and 2) a value transfer method that leverages value information extracted from
a simple classifier trained using representations from the target language
model. Our experiments applying TS-DShapley to select data for fine-tuning
BERT-based language models on benchmark natural language understanding (NLU)
datasets show that TS-DShapley outperforms existing data selection methods.
Further, TS-DShapley can filter fine-tuning data to increase language model
performance compared to training with the full fine-tuning dataset.