Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals
Open access
Date
2023-07Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We present a human-in-the-loop dashboard tailored to diagnosing potential spurious features that NLI models rely on for predictions. The dashboard enables users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions. Additionally, users can receive feedback from a trained NLI model on how challenging the newly created example is and make refinements based on the feedback. Through our investigation, we discover several categories of spurious correlations that impact the reasoning of NLI models, which we group into three categories: Semantic Relevance, Logical Fallacies, and Bias. Based on our findings, we identify and describe various research opportunities, including diversifying training data and assessing NLI models’ robustness by creating adversarial test suites. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000637530Publication status
publishedExternal links
Book title
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 3: System DemonstrationsPages / Article No.
Publisher
Association for Computational LinguisticsEvent
Organisational unit
02219 - ETH AI Center / ETH AI Center
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ETH Bibliography
yes
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