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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.
Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
Incorporating natural language rationales in the prompt and In-Context Learning (ICL) has led to a significant improvement of Large Language Models (LLMs) performance.
TIGTEC : Token Importance Guided TExt Counterfactuals
Counterfactual examples explain a prediction by highlighting changes of instance that flip the outcome of a classifier.
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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