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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 13·5 min read
Explainability in Practice: A Survey of Explainable NLP Across Various Domains
TL;DR
This survey reviews the state of explainable natural language processing (XNLP) across seven critical domains, highlighting the need for transparency in AI models like GPT-4o and BERT. It proposes a two-tier evaluation protocol to assess explanation methods and identifies gaps in current research.
✦ Why It Matters
Evaluate your NLP models today using the proposed two-tier evaluation protocol for better transparency.
Key Takeaways
- Assess the need for explanations in your specific application domain.
- Implement the proposed two-tier evaluation protocol for your models.
- Explore personalized explanations to enhance user understanding.
- Consider human judgment in evaluating the effectiveness of explanations.
Full Summary
Explainable natural language processing (XNLP) is crucial as NLP models are increasingly used in sectors like healthcare and finance, where decisions can significantly impact lives. This survey examines XNLP across seven domains: medicine, finance, systematic reviews, customer relationship management, chatbots, social and behavioral science, and human resources.
It evaluates the types of explanations needed, the methods employed, and their effectiveness. A structured synthesis reveals divergent requirements across domains and compares explanation methods based on their scope, faithfulness, and computational cost.
The authors propose a two-tier evaluation protocol that distinguishes between shared technical metrics and domain-specific validation. Additionally, the review highlights underrepresented areas in XNLP literature, such as real-world applicability and the importance of human judgment in evaluating explanations, while suggesting future research directions like personalized explanations and mechanistic interpretability for large language models.