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

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hadi Mohammadi, Robert A. Bagheri, Anastasia Giachanou, Daniel L. Oberski ·

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

    arXiv:2502.00837v3 Announce Type: replace-cross Abstract: Natural Language Processing (NLP) is now embedded in critical sectors including healthcare, finance, and customer relationship management, where models such as GPT-4o, Gemini, and BERT increasingly inform decisions. The bl…

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