Explainability in Practice: A Survey of Explainable NLP Across Various Domains

2025-02-02

AI Summary

Black-box NLP models (e.g., GPT, BERT) lack sufficient interpretability for high-stakes applications in healthcare, finance, and customer management. Method: This paper conducts cross-domain eXplainable Natural Language Processing (XNLP) practice research, integrating model-agnostic explanation techniques (LIME/SHAP), attention visualization, counterfactual reasoning, and human factors evaluation experiments to establish, for the first time, a comprehensive XNLP practice framework aligned with real-world deployment scenarios. Contributions: (1) Proposes industry-tailored XNLP design principles and evaluation guidelines; (2) Systematically identifies and addresses three critical research gaps—real-world applicability, human-AI collaborative evaluation, and quantitative explainability metrics; and (3) Clarifies domain-specific core explanation requirements, thereby advancing XNLP from theoretical validation toward high-reliability, production-ready deployment.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.

Problem

• Interpretable NLP
• Black-box Models
• Decision Transparency

Innovation

• Interpretable Natural Language Processing (XNLP)
• Domain-specific Design Strategies
• Practical Evaluation and Human Role