@inproceedings{bhagat-etal-2025-richer,
title = "Richer Output for Richer Countries: Uncovering Geographical Disparities in Generated Stories and Travel Recommendations",
author = "Bhagat, Kirti and
Vasisht, Kinshuk and
Pruthi, Danish",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.262/",
doi = "10.18653/v1/2025.findings-naacl.262",
pages = "4645--4653",
ISBN = "979-8-89176-195-7",
abstract = "While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographical knowledge (or lack thereof) on real-world applications has not been documented. In this work, we examine large language models for two common scenarios that require geographical knowledge: (a) travel recommendations and (b) geo-anchored story generation. Specifically, we study five popular language models, and across about 100K travel requests, and 200K story generations, we observe that travel recommendations corresponding to poorer countries are less unique with fewer location references, and stories from these regions more often convey emotions of hardship and sadness compared to those from wealthier nations."
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<abstract>While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographical knowledge (or lack thereof) on real-world applications has not been documented. In this work, we examine large language models for two common scenarios that require geographical knowledge: (a) travel recommendations and (b) geo-anchored story generation. Specifically, we study five popular language models, and across about 100K travel requests, and 200K story generations, we observe that travel recommendations corresponding to poorer countries are less unique with fewer location references, and stories from these regions more often convey emotions of hardship and sadness compared to those from wealthier nations.</abstract>
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%0 Conference Proceedings
%T Richer Output for Richer Countries: Uncovering Geographical Disparities in Generated Stories and Travel Recommendations
%A Bhagat, Kirti
%A Vasisht, Kinshuk
%A Pruthi, Danish
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F bhagat-etal-2025-richer
%X While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographical knowledge (or lack thereof) on real-world applications has not been documented. In this work, we examine large language models for two common scenarios that require geographical knowledge: (a) travel recommendations and (b) geo-anchored story generation. Specifically, we study five popular language models, and across about 100K travel requests, and 200K story generations, we observe that travel recommendations corresponding to poorer countries are less unique with fewer location references, and stories from these regions more often convey emotions of hardship and sadness compared to those from wealthier nations.
%R 10.18653/v1/2025.findings-naacl.262
%U https://aclanthology.org/2025.findings-naacl.262/
%U https://doi.org/10.18653/v1/2025.findings-naacl.262
%P 4645-4653
Markdown (Informal)
[Richer Output for Richer Countries: Uncovering Geographical Disparities in Generated Stories and Travel Recommendations](https://aclanthology.org/2025.findings-naacl.262/) (Bhagat et al., Findings 2025)
ACL