Papers by Michele Donini
On the Lack of Robust Interpretability of Neural Text Classifiers (2021.findings-acl)
Copied to clipboard
Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cedric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
| Challenge: | Several models have been proposed to interpret models with feature-based interpretability methods. |
| Approach: | They propose to quantify the robustness of neural text classifiers by using two randomization tests to compare models with identical initializations. |
| Outcome: | The proposed methods show surprising deviations from expected behavior . the results raise questions about the extent of insights that practitioners may draw from interpretations. |
Geographical Erasure in Language Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models encode vast amounts of world knowledge but are at risk of inordinately capturing information about dominant groups. |
| Approach: | They propose to operationalise a form of geographical erasure wherein language models underpredict certain countries. |
| Outcome: | The proposed model underpredicts certain countries by a factor 3 . the model is based on large datasets and is able to mitigate the effects . |