Papers with model stability issues
Investigating the Impact of Model Instability on Explanations and Uncertainty (2024.findings-acl)
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| Challenge: | Explainable AI methods are typically evaluated holistically, but small perturbations to inputs can vastly distort explanations. |
| Approach: | They artificially simulate epistemic uncertainty in text input by introducing noise at inference time and measure the effect on the output of pre-trained language models. |
| Outcome: | The proposed model can detect salient tokens when uncertain, but it is not reliable when small perturbations are exposed during training. |