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NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research

arXiv 2507.10559 ↗First posted 2 Jul 2025cs.CY cs.AI cs.CLcited by 0

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Abstract (arXiv)

Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to share their knowledge and views with a wide audience. Recognizing the opportunities of the present, for both the research field and for individual researchers, this paper shares recommendations for communicating with a general audience about the capabilities and limitations of NLP. These recommendations cover three themes: vague terminology as an obstacle to public understanding, unreasonable expectations as obstacles to sustainable growth, and ethical failures as obstacles to continued support. Published NLP research and popular news coverage are cited to illustrate these themes with examples. The recommendations promote effective, transparent communication with the general public about NLP, in order to strengthen public understanding and encourage support for research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 53 references that are in the map. First posted on arXiv 2 Jul 2025.

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10 of 53 references.

References · 10

  1. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  2. 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexitycited by 302
  3. 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 106
  4. 2020What Can We Do to Improve Peer Review in NLP?cited by 32
  5. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 26
  6. 2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecuritycited by 21
  7. 2024From Insights to Actions: The Impact of Interpretability and Analysis Research on NLPcited by 20
  8. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  9. 2025Stop treating `AGI' as the north-star goal of AI researchcited by 11
  10. 2023Teaching Computer Science Students to Communicate Scientific Findings More Effectivelycited by 1

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