ExplainableNLP

ExplainableNLP is a field of study within natural language processing that focuses on making the predictions, decisions, and internal mechanisms of NLP models understandable to humans. Its aims include transparency, trust, accountability, and the ability to debug or contest outputs. The field encompasses both inherently interpretable models and post hoc explanations of complex, opaque systems such as deep neural networks.

Inherently interpretable approaches seek models whose behavior is directly understandable, such as linear classifiers with human-friendly features or rule-based systems where the reasoning can be traced step by step. This category emphasizes simplicity and explainability at the cost of some performance on certain tasks.

Post hoc explainability generates explanations for black-box models after training. Common techniques include token-level feature attribution (which words contributed most to a decision), gradient-based methods such as integrated gradients, perturbation-based methods like LIME, and model-agnostic approaches such as SHAP. Other methods include attention visualization, surrogate models, and example-based explanations.

Evaluation in explainableNLP involves faithfulness, which measures how accurately explanations reflect model behavior, and human-centered assessments of usefulness and understandability. Standard benchmarks are evolving, with attention to plausibility, stability, and cross-domain and cross-language explanations. Challenges include balancing accuracy with interpretability, avoiding misleading or brittle explanations, handling biases, and ensuring explanations generalize across languages and domains.

Applications span healthcare, finance, law, and other regulated sectors where explanations support accountability, adjudication, and user trust. Ongoing work addresses better metrics for faithfulness, causal explanations, multilingual explainability, and the integration of explanations into user interfaces and model cards for transparency.