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Mitigating Text Toxicity with Counterfactual Generation
In this work, we propose to apply counterfactual generation methods from the eXplainable AI (XAI) field to target and mitigate textual toxicity.
ClimateQ&A: Bridging the gap between climate scientists and the general public
This research paper investigates public views on climate change and biodiversity loss by analyzing questions asked to the ClimateQ&A platform.
Evaluating self-attention interpretability through human-grounded experimental protocol
A human-grounded experiment is conducted to evaluate and compare CLS-A to other interpretability methods.
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