mlsanityData to 7 Oct 2026

How Much Consistency Is Your Accuracy Worth?

arXiv 2310.13781 ↗First posted 20 Oct 2023cs.CLcited by 1

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Abstract (arXiv)

Contrast set consistency is a robustness measurement that evaluates the rate at which a model correctly responds to all instances in a bundle of minimally different examples relying on the same knowledge. To draw additional insights, we propose to complement consistency with relative consistency -- the probability that an equally accurate model would surpass the consistency of the proposed model, given a distribution over possible consistencies. Models with 100% relative consistency have reached a consistency peak for their accuracy. We reflect on prior work that reports consistency in contrast sets and observe that relative consistency can alter the assessment of a model's consistency compared to another. We anticipate that our proposed measurement and insights will influence future studies aiming to promote consistent behavior in models.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 20 Oct 2023.

Builds on

18 of 26 references.

References · 18

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  10. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
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  12. 2019"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understandingcited by 121
6 more
  1. 2019Reasoning Over Paragraph Effects in Situationscited by 79
  2. 2019Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claimscited by 73
  3. 2019A Logic-Driven Framework for Consistency of Neural Modelscited by 64
  4. 2022UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Trainingcited by 57
  5. 2022CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negationcited by 38
  6. 2021Learning with Instance Bundles for Reading Comprehensioncited by 8

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