@inproceedings{locatelli-etal-2025-ai,
title = "{AI} and Climate Change Discourse: What Opinions Do Large Language Models Present?",
author = "Locatelli, Marcelo Sartori and
Dutenhefner, Pedro and
Buzelin, Arthur and
Alzamora, Pedro Loures and
Aquino, Yan and
Bento, Pedro Augusto Torres and
Malaquias, Samira and
Estanislau, Victoria and
Santana, Caio and
Dayrell, Lucas and
Vasconcelos, Marisa Affonso and
Jr., Wagner Meira and
Almeida, Virgilio",
editor = "Dutia, Kalyan and
Henderson, Peter and
Leippold, Markus and
Manning, Christoper and
Morio, Gaku and
Muccione, Veruska and
Ni, Jingwei and
Schimanski, Tobias and
Stammbach, Dominik and
Singh, Alok and
Su, Alba (Ruiran) and
A. Vaghefi, Saeid",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.climatenlp-1.8/",
doi = "10.18653/v1/2025.climatenlp-1.8",
pages = "113--125",
ISBN = "979-8-89176-259-6",
abstract = "Large Language Models (LLMs) are increasingly used in applications that shape public discourse, yet little is known aboutwhether they reflect distinct opinions on global issues like climate change. This study compares climate change-relatedresponses from multiple LLMs with human opinions collected through the People{'}s Climate Vote 2024 survey (UNDP {--} UnitedNations Development Programme and Oxford, 2024). We compare country and LLM{''}s answer probability distributions and apply Exploratory Factor Analysis (EFA) to identify latent opinion dimensions. Our findings reveal that while LLM responsesdo not exhibit significant biases toward specific demographic groups, they encompass a wide range of opinions, sometimesdiverging markedly from the majority human perspective."
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<abstract>Large Language Models (LLMs) are increasingly used in applications that shape public discourse, yet little is known aboutwhether they reflect distinct opinions on global issues like climate change. This study compares climate change-relatedresponses from multiple LLMs with human opinions collected through the People’s Climate Vote 2024 survey (UNDP – UnitedNations Development Programme and Oxford, 2024). We compare country and LLM”s answer probability distributions and apply Exploratory Factor Analysis (EFA) to identify latent opinion dimensions. Our findings reveal that while LLM responsesdo not exhibit significant biases toward specific demographic groups, they encompass a wide range of opinions, sometimesdiverging markedly from the majority human perspective.</abstract>
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%0 Conference Proceedings
%T AI and Climate Change Discourse: What Opinions Do Large Language Models Present?
%A Locatelli, Marcelo Sartori
%A Dutenhefner, Pedro
%A Buzelin, Arthur
%A Alzamora, Pedro Loures
%A Aquino, Yan
%A Bento, Pedro Augusto Torres
%A Malaquias, Samira
%A Estanislau, Victoria
%A Santana, Caio
%A Dayrell, Lucas
%A Vasconcelos, Marisa Affonso
%A Jr., Wagner Meira
%A Almeida, Virgilio
%Y Dutia, Kalyan
%Y Henderson, Peter
%Y Leippold, Markus
%Y Manning, Christoper
%Y Morio, Gaku
%Y Muccione, Veruska
%Y Ni, Jingwei
%Y Schimanski, Tobias
%Y Stammbach, Dominik
%Y Singh, Alok
%Y Su, Alba (Ruiran)
%Y A. Vaghefi, Saeid
%S Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-259-6
%F locatelli-etal-2025-ai
%X Large Language Models (LLMs) are increasingly used in applications that shape public discourse, yet little is known aboutwhether they reflect distinct opinions on global issues like climate change. This study compares climate change-relatedresponses from multiple LLMs with human opinions collected through the People’s Climate Vote 2024 survey (UNDP – UnitedNations Development Programme and Oxford, 2024). We compare country and LLM”s answer probability distributions and apply Exploratory Factor Analysis (EFA) to identify latent opinion dimensions. Our findings reveal that while LLM responsesdo not exhibit significant biases toward specific demographic groups, they encompass a wide range of opinions, sometimesdiverging markedly from the majority human perspective.
%R 10.18653/v1/2025.climatenlp-1.8
%U https://aclanthology.org/2025.climatenlp-1.8/
%U https://doi.org/10.18653/v1/2025.climatenlp-1.8
%P 113-125
Markdown (Informal)
[AI and Climate Change Discourse: What Opinions Do Large Language Models Present?](https://aclanthology.org/2025.climatenlp-1.8/) (Locatelli et al., ClimateNLP 2025)
ACL
- Marcelo Sartori Locatelli, Pedro Dutenhefner, Arthur Buzelin, Pedro Loures Alzamora, Yan Aquino, Pedro Augusto Torres Bento, Samira Malaquias, Victoria Estanislau, Caio Santana, Lucas Dayrell, Marisa Affonso Vasconcelos, Wagner Meira Jr., and Virgilio Almeida. 2025. AI and Climate Change Discourse: What Opinions Do Large Language Models Present?. In Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025), pages 113–125, Vienna, Austria. Association for Computational Linguistics.