Papers by Sara Marjanovic
DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models (2024.findings-emnlp)
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| Challenge: | LMs are useful in a variety of downstream applications from summarization to fact-checking, often relying on factual knowledge memorized during pre-training. |
| Approach: | They use two knowledge conflict measures and a novel dataset DYNAMICQA to examine the effect of intra-memory conflict on LMs' ability to accept contextual knowledge. |
| Outcome: | The proposed model can accept contextual knowledge with a higher degree of accuracy than models with fewer truth values. |
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. |