Explaining Language Models' Predictions with High-Impact Concepts

Understand

The emergence of large-scale pretrained language models has posed unprecedented challenges in deriving explanations of why the model has made some predictions.

• Stemmed from the compositional nature of languages, spurious correlations have further undermined the trustworthiness of NLP systems, leading to unreliable model explanations that are merely correlated with the output predictions.
• To encourage fairness and transparency, there exists an urgent demand for reliable explanations that allow users to consistently understand the model's behavior.
• In this work, we propose a complete framework for extending concept-based interpretability methods to NLP.