Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates

• URL: http://arxiv.org/abs/2110.03212v1
• Date: Thu, 7 Oct 2021 06:59:46 GMT
• Title: Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates
• Authors: Xiaochuang Han, Yulia Tsvetkov
• Abstract summary: influence tuning can help deconfounding the model from spurious patterns in data. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data.
• Score: 26.527311287924995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically "unlearn" confounding features. In this work, we propose influence tuning--a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.

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