Abstract
Recent advances in large language models (LLMs) have rapidly increased the public's interest in natural language processing (NLP). This attention is reflected by news outlets inviting NLP researchers to share their knowledge and views with public audiences. Recognizing the opportunities for the research field and for individual researchers, this paper shares recommendations for speaking with news media about NLP's capabilities and limitations. Drawing on the topical structure of research communication frameworks published by the US National Academies of Sciences, Engineering, and Medicine and the European Union's QUEST project, recommendations are provided for three field-appropriate communication challenges: cognitive terminology, unsustainable expectations, and ethical failures. Published NLP research and popular news coverage are cited to provide examples. The recommendations promote effective, transparent communication with the general public about NLP, in order to strengthen public understanding and encourage support for research.
More in this topicScientific communication studies how research is written, shared, translated, visualized, and understood by specialist and public audiences.
See all 50 papers → Apr 1, 2026
Recent advances in language models have prompted a growing body of work that questions prevailing evaluation practices. However, many such critiques have already been discussed in natural language processing (NLP), a field with a long history of methodological reflection on evaluation. We conduct a scoping review spanning four decades and develop a taxonomy of 22 recurring concerns. For each concern, we examine how recently it was discussed in the reviewed literature and whether it receives explicit attention in institutional checklists, finding that some concerns have become less visible in literature and many lack coverage in these checklists. Our work provides an overview of the major concerns in evaluation methodology that have shaped the field and identifies potential targets for renewed investigation and field-level intervention by the research community.
Ruchira Dhar, Qiwei Peng, Anders Søgaard
Department of Computer Science, University of Copenhagen
Sep 14, 2026
Since late 2022, a Limitations section has become mandatory at many top-tier NLP conferences. The growing number of accepted papers at these venues has resulted in a vast corpus of self-reported limitations that cannot all be manually reviewed, yet remains systematically unanalyzed. Therefore, in this paper, we conduct a large-scale analysis of the Limitations sections from ACL and EMNLP papers published between 2020 and 2025 to understand what researchers disclose about their own work. To do so, we implement a novel human-AI framework for iterative hybrid qualitative coding. This framework enables us to investigate trends in self-reported limitations over time, their correlations with specific paper attributes, and the writing patterns that recur around these disclosures. Our findings offer a critical reflection on the diverse reported challenges as well as the self-reporting practices of researchers in the NLP community.
Tawan Thaepprasit, Peeranuth Kehasukcharoen, Ding Wang +3
Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand · Google Research
Mar 29, 2026
In this paper, we discuss the relationship between natural language processing by computers (NLP) and the understanding of the human language capacity, as studied by linguistics and cognitive science. We outline the evolution of NLP from its beginnings until the age of large language models, and highlight for each of its main paradigms some similarities and differences with theories of the human language capacity. We conclude that the evolution of language technology has not substantially deepened our understanding of how human minds process natural language, despite the impressive language abilities attained by current chatbots using artificial neural networks.
Andrei Popescu-Belis
HEIG-VD / HES-SO, 1401 Yverdon-les-Bains, Switzerland · EPFL, 1015 Lausanne, Switzerland