@inproceedings{bassignana-etal-2026-seal,
title = "{SEAL}: {S}ocio{E}conomic Alignment in {LLM}s",
author = {Bassignana, Elisa and
Calvo-Bartolom{\'e}, Lorena and
D{\"o}nmez, Esra and
Lutz, Marlene and
Muti, Arianna and
Neplenbroek, Vera and
Rooein, Donya},
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.34/",
pages = "418--434",
ISBN = "979-8-89176-495-8",
abstract = "According to the United Nations Sustainable Development Goals, socioeconomic inequality is one of the defining challenges of the 21st century, as it shapes access to resources and opportunities across societies worldwide. As large language models (LLMs) become widely used for everyday tasks, they have the potential to create positive social impact by expanding access to information, education, writing support, employment guidance, and administrative assistance. However, this potential depends on whether such systems perform equally well for users from different socioeconomic backgrounds. In this paper, we evaluate LLM-generated responses to prompts written by users across socioeconomic groups (lower, middle, and higher), examining disparities in helpfulness, sycophancy, human likeness, emotion, and text complexity. We find differences in quality of service across socioeconomic statuses (SES): lower-SES groups receive responses that are less helpful, less complex, less assertive, more negative in sentiment, less trust-oriented, and more sycophantically validating."
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%0 Conference Proceedings
%T SEAL: SocioEconomic Alignment in LLMs
%A Bassignana, Elisa
%A Calvo-Bartolomé, Lorena
%A Dönmez, Esra
%A Lutz, Marlene
%A Muti, Arianna
%A Neplenbroek, Vera
%A Rooein, Donya
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F bassignana-etal-2026-seal
%X According to the United Nations Sustainable Development Goals, socioeconomic inequality is one of the defining challenges of the 21st century, as it shapes access to resources and opportunities across societies worldwide. As large language models (LLMs) become widely used for everyday tasks, they have the potential to create positive social impact by expanding access to information, education, writing support, employment guidance, and administrative assistance. However, this potential depends on whether such systems perform equally well for users from different socioeconomic backgrounds. In this paper, we evaluate LLM-generated responses to prompts written by users across socioeconomic groups (lower, middle, and higher), examining disparities in helpfulness, sycophancy, human likeness, emotion, and text complexity. We find differences in quality of service across socioeconomic statuses (SES): lower-SES groups receive responses that are less helpful, less complex, less assertive, more negative in sentiment, less trust-oriented, and more sycophantically validating.
%U https://aclanthology.org/2026.nlp4pi-1.34/
%P 418-434
Markdown (Informal)
[SEAL: SocioEconomic Alignment in LLMs](https://aclanthology.org/2026.nlp4pi-1.34/) (Bassignana et al., NLP4PI 2026)
ACL
- Elisa Bassignana, Lorena Calvo-Bartolomé, Esra Dönmez, Marlene Lutz, Arianna Muti, Vera Neplenbroek, and Donya Rooein. 2026. SEAL: SocioEconomic Alignment in LLMs. In Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI), pages 418–434, Budapest, Hungary. Association for Computational Linguistics.