When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD

• URL: http://arxiv.org/abs/2503.18290v1
• Date: Mon, 24 Mar 2025 02:24:18 GMT
• Title: When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD
• Authors: Paul K. Mandal,
• Abstract summary: I partition SQuAD into easy-to-learn, ambiguous, and hard-to-learn subsets.<n>I then compare the performance of models trained on these subsets to those trained on randomly selected samples of equal size.<n>Results show that training on cartography-based subsets does not improve generalization to the SQuAD validation set or the AddSent adversarial set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, I investigate the effectiveness of dataset cartography for extractive question answering on the SQuAD dataset. I begin by analyzing annotation artifacts in SQuAD and evaluate the impact of two adversarial datasets, AddSent and AddOneSent, on an ELECTRA-small model. Using training dynamics, I partition SQuAD into easy-to-learn, ambiguous, and hard-to-learn subsets. I then compare the performance of models trained on these subsets to those trained on randomly selected samples of equal size. Results show that training on cartography-based subsets does not improve generalization to the SQuAD validation set or the AddSent adversarial set. While the hard-to-learn subset yields a slightly higher F1 score on the AddOneSent dataset, the overall gains are limited. These findings suggest that dataset cartography provides little benefit for adversarial robustness in SQuAD-style QA tasks. I conclude by comparing these results to prior findings on SNLI and discuss possible reasons for the observed differences.

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