Neighboring Words Affect Human Interpretation of Saliency Explanations
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Abstract (arXiv)
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. We conduct a user study to investigate how the marking of a word's neighboring words affect the explainee's perception of the word's importance in the context of a saliency explanation. We find that neighboring words have significant effects on the word's importance rating. Concretely, we identify that the influence changes based on neighboring direction (left vs. right) and a-priori linguistic and computational measures of phrases and collocations (vs. unrelated neighboring words). Our results question whether text-based saliency explanations should be continued to be communicated at word level, and inform future research on alternative saliency explanation methods.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 4 May 2023.
Builds on
23 of 42 references.
References · 23
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11 more
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Built on by
Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2024Layer-Wise Relevance Propagation with Conservation Property for ResNetcited by 7
- 2024Evaluating Saliency Explanations in NLP by Crowdsourcingcited by 1
- 2025Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cuescited by 0
- 2025Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attributioncited by 0
- 2025Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution11 Dec 2025
- 2025Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues17 Nov 2025
- 2024Layer-Wise Relevance Propagation with Conservation Property for ResNet12 Jul 2024
- 2024Evaluating Saliency Explanations in NLP by Crowdsourcing17 May 2024