Towards LLM-guided Causal Explainability for Black-box Text Classifiers

Understand

With the advent of larger and more complex deep learning models, such as in Natural Language Processing (NLP), model qualities like explainability and interpretability, albeit highly desirable, are becoming harder challenges to tackle and solve.

• For example, state-of-the-art models in text classification are black-box by design.
• Although standard explanation methods provide some degree of explainability, these are mostly correlation-based methods and do not provide much insight into the model.
• The alternative of causal explainability is more desirable to achieve but extremely challenging in NLP due to a variety of reasons.