@inproceedings{leiter-etal-2026-excam,
title = "{E}x{CAM}: Explainable Cultural Awareness Metrics",
author = "Leiter, Christoph and
Song, Haiyue and
Kaing, Hour and
Tei, Jin and
Tanaka, Hideki and
Utiyama, Masao and
Eger, Steffen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2473/",
pages = "53202--53230",
ISBN = "979-8-89176-462-0",
abstract = "Evaluating the cultural awareness of large language models is crucial to ensure the fairness of generated text and the generalizability of applications across the world. Recent benchmarks explore cultural goods like food or values like behavior in stressful situations through the lens of question answering or text generation tasks. However, creating these benchmarks requires time-intensive and costly human annotations. Also, benchmarks that evaluate cultural awareness in free text are scarce and often rely on dated evaluation mechanisms. To address this gap, we introduce ExCAM, an **Ex**plainable **C**ultural **A**wareness **M**etric, which is, to our knowledge, the first dedicated evaluation metric that identifies, rates and explains cultural errors in instruction-output pairs. To train and evaluate ExCAM, we introduce ExCAM40k, a dataset comprised of nine existing benchmarks that we reformat and enhance with synthetic errors. Compared to several baselines, including GPT-5, ExCAM achieves the highest error detection rate with up to 80{\%} accuracy on a balanced test set. Therefore, ExCAM opens the pathway towards fine-grained and explainable cultural evaluation of free text."
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<abstract>Evaluating the cultural awareness of large language models is crucial to ensure the fairness of generated text and the generalizability of applications across the world. Recent benchmarks explore cultural goods like food or values like behavior in stressful situations through the lens of question answering or text generation tasks. However, creating these benchmarks requires time-intensive and costly human annotations. Also, benchmarks that evaluate cultural awareness in free text are scarce and often rely on dated evaluation mechanisms. To address this gap, we introduce ExCAM, an **Ex**plainable **C**ultural **A**wareness **M**etric, which is, to our knowledge, the first dedicated evaluation metric that identifies, rates and explains cultural errors in instruction-output pairs. To train and evaluate ExCAM, we introduce ExCAM40k, a dataset comprised of nine existing benchmarks that we reformat and enhance with synthetic errors. Compared to several baselines, including GPT-5, ExCAM achieves the highest error detection rate with up to 80% accuracy on a balanced test set. Therefore, ExCAM opens the pathway towards fine-grained and explainable cultural evaluation of free text.</abstract>
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%0 Conference Proceedings
%T ExCAM: Explainable Cultural Awareness Metrics
%A Leiter, Christoph
%A Song, Haiyue
%A Kaing, Hour
%A Tei, Jin
%A Tanaka, Hideki
%A Utiyama, Masao
%A Eger, Steffen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F leiter-etal-2026-excam
%X Evaluating the cultural awareness of large language models is crucial to ensure the fairness of generated text and the generalizability of applications across the world. Recent benchmarks explore cultural goods like food or values like behavior in stressful situations through the lens of question answering or text generation tasks. However, creating these benchmarks requires time-intensive and costly human annotations. Also, benchmarks that evaluate cultural awareness in free text are scarce and often rely on dated evaluation mechanisms. To address this gap, we introduce ExCAM, an **Ex**plainable **C**ultural **A**wareness **M**etric, which is, to our knowledge, the first dedicated evaluation metric that identifies, rates and explains cultural errors in instruction-output pairs. To train and evaluate ExCAM, we introduce ExCAM40k, a dataset comprised of nine existing benchmarks that we reformat and enhance with synthetic errors. Compared to several baselines, including GPT-5, ExCAM achieves the highest error detection rate with up to 80% accuracy on a balanced test set. Therefore, ExCAM opens the pathway towards fine-grained and explainable cultural evaluation of free text.
%U https://aclanthology.org/2026.findings-emnlp.2473/
%P 53202-53230
Markdown (Informal)
[ExCAM: Explainable Cultural Awareness Metrics](https://aclanthology.org/2026.findings-emnlp.2473/) (Leiter et al., Findings 2026)
ACL
- Christoph Leiter, Haiyue Song, Hour Kaing, Jin Tei, Hideki Tanaka, Masao Utiyama, and Steffen Eger. 2026. ExCAM: Explainable Cultural Awareness Metrics. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 53202–53230, Budapest, Hungary. Association for Computational Linguistics.