On the Transferability of Minimal Prediction Preserving Inputs in Question Answering
Shayne Longpre, Yi Lu, Christopher DuBois · 2021
Recent work (Feng et al., 2018) establishes the presence of short, uninterpretable input fragments that yield high confidence and accuracy in neural models.We refer to these as Minimal Prediction Preserving Inputs (MPPIs).In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and "dataset bias" (where a model learns to attend to spurious, non-generalizable cues in the training data).We discover a perplexing invariance of MPPIs to random training seed, model architecture, pretraining, and training domain.MPPIs demonstrate remarkable transferability across domains -achieving significantly higher performance than comparably short queries.Additionally, penalizing over-confidence on MPPIs fails to improve either generalization or adversarial robustness.These results suggest the interpretability of MPPIs is insufficient to characterize generalization capacity of these models.We hope this focused investigation encourages more systematic analysis of model behavior outside of the human interpretable distribution of examples.* equal contribution 1 For question answering we construct MPPIs by only removing words from the query.Modifying the context paragraph is poorly defined in MPPI generation as it perturbs the output space, rendering an answer impossible or trivial.