Gradient-based Analysis of NLP Models is Manipulable
Junlin Wang, Jens Tuyls, Eric W. Wallace, Sameer Kumar Singh · 2020
Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, their faithfulness.In this paper, however, we demonstrate that the gradients of a model are easily manipulable, and thus bring into question the reliability of gradient-based analyses.In particular, we merge the layers of a target model with a FACADE model that overwhelms the gradients without affecting the predictions.This FACADE model can be trained to have gradients that are misleading and irrelevant to the task, such as focusing only on the stop words in the input.On a variety of NLP tasks (text classification, NLI, and QA), we show that our method can manipulate numerous gradient-based analysis techniques: saliency maps, input reduction, and adversarial perturbations all identify unimportant or targeted tokens as being highly important.The code and a tutorial of this paper is available at http://ucinlp.github.io/facade.Input Prediction P(+)=0.9How !great Gradient How great !(a) Original Model, forig Prediction P(+)=0.5 Gradient How great !Input How !great (b) FACADE Model, g 1024 Input Prediction P(+)=0.9Gradient How great !