ACL 2026 paper proposes two scores to tell when VLM explanations are trustworthy
BrihiJ · x · 2026-07-27
The paper argues that VLM explanations can sound convincing even when the prediction is wrong, so users need better ways to judge whether to trust them.
It introduces two VLM-centric explanation quality scores:
- Visual Fidelity: whether the explanation stays faithful to the image.
- Contrastiveness: whether the explanation rules out alternative answers.
The figure contrasts these with existing explanation metrics, which can look strong without correlating well with actual VLM accuracy. The authors say the new scores help users better tell correct VLM answers from incorrect ones.
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