Combining Feature and Instance Attribution to Detect Artifacts

By Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, Byron C. Wallace. 2021

48 citations recorded by the source corpus.

Training the deep neural networks that dominate NLP requires large datasets. These are often collected automatically or via crowdsourcing, and may exhibit systematic biases or annotation artifacts. By the latter we mean spurious correlations between inputs and outputs that do not represent a generally held causal relationship between features and classes; models that exploit such correlations may appear to perform a given task well, but fail on out of sample data. In this paper, we evaluate use of different attribution methods for aiding identification of training data artifacts. We propose new hybrid approaches that combine saliency maps (which highlight important input features) with instance attribution methods (which retrieve training samples influential to a given prediction). We show that this proposed training-feature attribution can be used to efficiently uncover artifacts in training data when a challenging validation set is available. We also carry out a small user study to evaluate whether these methods are useful to NLP researchers in practice, with promising results. We make code for all methods and experiments in this paper available.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

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Citations in the corpus

Understanding Dataset Difficulty with V-Usable Information (2021, neutral): “Our approach to discovering dataset arte-facts can also complement existing approaches to artefact discovery (Gardner et al., 2021; Pezeshkpour et al., 2021; Le Bras et al., 2020).”
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers (2021, positive): “Global interpretability methods are useful not only to facilitate explainability, but also to detect and mitigate artifacts in data (Pezeshkpour et al., 2021; Han and Tsvetkov, 2021).”
A Review on Language Models as Knowledge Bases (2022, methodology): “Gradient-based and non-Hessian information inﬂuence attribution methods (Pezeshkpour et al., 2021b) have been introduced to speed up computation.”
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates (2021, methodology): “, 2020), or through a combination of thereof (Pezeshkpour et al., 2021a).”
Information-Theoretic Measures of Dataset Difficulty (2021, unknown): “Our approach to discovering dataset artefacts can also complement existing approaches to artefact discovery (Gardner et al., 2021; Pezeshkpour et al., 2021).”