Unlearning Reveals the Influential Training Data of Language Models
Masaru Isonuma Ivan Titov
In order to enhance the performance of language models while mitigating the
risks of generating harmful content, it is crucial to identify which training
dataset affects the model's outputs. Ideally, we can measure the influence of
each dataset by removing it from training; however, it is prohibitively
expensive to retrain a model multiple times. This paper presents UnTrac, which
estimates the influence of a training dataset by unlearning it from the trained
model. UnTrac is extremely simple; each training dataset is unlearned by
gradient ascent, and we evaluate how much the model's predictions change after
unlearning. We empirically examine if our methods can assess the influence of
pretraining datasets on generating toxic, biased, and untruthful content.
Experimental results demonstrate that our method estimates their influence much
more accurately than existing methods while requiring neither excessive memory
space nor multiple model checkpoints.