Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods

Feldhus, Nils; Hennig, Leonhard; Nasert, Maximilian; Ebert, Christopher; Schwarzenberg, Robert; Mller, Sebastian

doi:10.18653/v1/2023.nlrse-1.4

Cited by 5 publications

(1 citation statement)

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“…One of the most closely related works found that feature-based explanations could help users simulate model predictions for search results [12]. Another similar study involves a verbal saliency map using a model-agnostic explainer and a human evaluation of explanation representations of news topic classifier and sentiment analysis [17]. Their finding is that the saliency map makes explanations more understandable and less cognitively challenging for humans than heatmap visualization.…”

Section: Related Workmentioning

confidence: 99%

Explaining Search Result Stances to Opinionated People

Wu,

Draws,

Cau

et al. 2023

Communications in Computer and Information Science

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Section: Related Workmentioning

confidence: 99%