@inproceedings{luo-etal-2026-ace,
title = "{ACE}-Align: Attribute Causal Effect Alignment for Cultural Values under Varying Persona Granularities",
author = "Luo, JiaTang and
Xu, Bingbing and
Chen, Rongxin and
Zhao, Xiaoyan and
Zhang, Yang and
Pang, Liang and
Huang, Zhiyong and
Shen, Huawei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1593/",
pages = "36216--36235",
ISBN = "979-8-89176-459-0",
abstract = "Ensuring that large language models (LLMs) reflect diverse cultural values is important for globally deployed NLP systems. However, existing approaches often treat cultural groups as homogeneous and overlook within-group heterogeneity arising from intersecting demographic attributes, leading to unstable behavior under varying persona granularity. To address this gap, we propose **ACE-Align** (**A**tribute **C**ausal **E**ffect Alignment), a causally inspired framework based on controlled persona edits that aligns how specific demographic attributes shift different cultural values, rather than treating each culture as a homogeneous group. We evaluate ACE-Align across 14 countries spanning five continents, with personas specified by subsets of four attributes (gender, education, residence, and marital status) and granularity instantiated by the number of specified attributes. Across all persona granularities, ACE-Align consistently outperforms baselines. Moreover, in within-survey comparisons, it reduces the average Global North{--}South alignment gap from 3.40 to 1.11 points on WVS and from 2.53 to 0.85 points on ISSP. Code and dataset are released at https://github.com/Wells-Luo/ACE-Align."
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<abstract>Ensuring that large language models (LLMs) reflect diverse cultural values is important for globally deployed NLP systems. However, existing approaches often treat cultural groups as homogeneous and overlook within-group heterogeneity arising from intersecting demographic attributes, leading to unstable behavior under varying persona granularity. To address this gap, we propose **ACE-Align** (**A**tribute **C**ausal **E**ffect Alignment), a causally inspired framework based on controlled persona edits that aligns how specific demographic attributes shift different cultural values, rather than treating each culture as a homogeneous group. We evaluate ACE-Align across 14 countries spanning five continents, with personas specified by subsets of four attributes (gender, education, residence, and marital status) and granularity instantiated by the number of specified attributes. Across all persona granularities, ACE-Align consistently outperforms baselines. Moreover, in within-survey comparisons, it reduces the average Global North–South alignment gap from 3.40 to 1.11 points on WVS and from 2.53 to 0.85 points on ISSP. Code and dataset are released at https://github.com/Wells-Luo/ACE-Align.</abstract>
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%0 Conference Proceedings
%T ACE-Align: Attribute Causal Effect Alignment for Cultural Values under Varying Persona Granularities
%A Luo, JiaTang
%A Xu, Bingbing
%A Chen, Rongxin
%A Zhao, Xiaoyan
%A Zhang, Yang
%A Pang, Liang
%A Huang, Zhiyong
%A Shen, Huawei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F luo-etal-2026-ace
%X Ensuring that large language models (LLMs) reflect diverse cultural values is important for globally deployed NLP systems. However, existing approaches often treat cultural groups as homogeneous and overlook within-group heterogeneity arising from intersecting demographic attributes, leading to unstable behavior under varying persona granularity. To address this gap, we propose **ACE-Align** (**A**tribute **C**ausal **E**ffect Alignment), a causally inspired framework based on controlled persona edits that aligns how specific demographic attributes shift different cultural values, rather than treating each culture as a homogeneous group. We evaluate ACE-Align across 14 countries spanning five continents, with personas specified by subsets of four attributes (gender, education, residence, and marital status) and granularity instantiated by the number of specified attributes. Across all persona granularities, ACE-Align consistently outperforms baselines. Moreover, in within-survey comparisons, it reduces the average Global North–South alignment gap from 3.40 to 1.11 points on WVS and from 2.53 to 0.85 points on ISSP. Code and dataset are released at https://github.com/Wells-Luo/ACE-Align.
%U https://aclanthology.org/2026.emnlp-main.1593/
%P 36216-36235
Markdown (Informal)
[ACE-Align: Attribute Causal Effect Alignment for Cultural Values under Varying Persona Granularities](https://aclanthology.org/2026.emnlp-main.1593/) (Luo et al., EMNLP 2026)
ACL
- JiaTang Luo, Bingbing Xu, Rongxin Chen, Xiaoyan Zhao, Yang Zhang, Liang Pang, Zhiyong Huang, and Huawei Shen. 2026. ACE-Align: Attribute Causal Effect Alignment for Cultural Values under Varying Persona Granularities. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36216–36235, Budapest, Hungary. Association for Computational Linguistics.