EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models

Multiple explainability methods exist, but choosing the right one matters: EvalxNLP integrates eight different explainability techniques, demonstrating that there is no one-size-fits-all approach. Organizations deploying NLP models in high-stakes applications need frameworks to systematically compare methods and select explanations tailored to their specific stakeholder needs.

Explainability can be evaluated on concrete, measurable properties: The framework assesses explanations based on key properties including faithfulness (whether explanations accurately reflect how the model works), plausibility (whether explanations make sense to humans), and complexity (how easy explanations are to understand). This structured approach helps move explainability from subjective to more objective evaluation.

Human satisfaction with explainability tools is achievable: Human evaluation results showed high user satisfaction with EvalxNLP, suggesting that well-designed explainability frameworks can effectively serve diverse user groups—an important finding for organizations seeking to build trust in AI systems through transparency.

Explainability tools should be accessible to non-experts: By offering a user-friendly Python framework with interactive, LLM-based textual explanations, EvalxNLP aims to "democratize explainability tools." This addresses a key responsible AI challenge: ensuring that stakeholders without deep technical expertise can understand and evaluate how NLP models make decisions.

Systematic comparison of explainability methods is now possible: The framework enables organizations to benchmark and compare different explanation approaches, supporting more rigorous and evidence-based decisions about which explainability techniques to deploy in real-world applications.