Meaningful Counterfactuals

Meaningful counterfactual explanations aim to improve the interpretability and trustworthiness of machine learning models, particularly large language models (LLMs), by generating realistic and insightful hypothetical scenarios that alter model predictions. Current research focuses on developing algorithms that produce grammatically correct and intuitively understandable counterfactuals, often incorporating optimization frameworks to ensure these scenarios remain plausible within the data distribution. This work addresses challenges in robustness and faithfulness, seeking to create explanations that are not only logically consistent but also reliably reflect the model's decision-making process, ultimately enhancing model understanding and facilitating more responsible AI development.