Survey details practical explainability for NLP models like GPT-4o

A new survey paper published on arXiv examines the practical application of explainable Natural Language Processing (XNLP) across various domains, including healthcare, finance, and customer service. The paper highlights the critical need for transparency in NLP models like GPT-4o, Gemini, and BERT, which are increasingly used for decision-making. It analyzes the types of explanations required, the methods employed, and evaluation strategies in seven distinct application areas, identifying gaps in current research regarding real-world applicability and human judgment. AI

IMPACT Highlights the need for transparency in NLP models and identifies research gaps for practical explainability.

RANK_REASON The item is a survey paper on arXiv detailing the practical application of explainable NLP. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• BERT
• chatbots
• customer relationship management
• Explainability in Practice: A Survey of Explainable NLP Across Various Domains
• explainable NLP
• finance
• Gemini
• GPT-4o
• Hadi Mohammadi
• Hugging Face
• human resources
• medicine
• Natural Language Processing

• paper
• other

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