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        <title>Country Is Not Enough: Recognition Action Gaps in Cultural Answerability</title>
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    <abstract>Large language models increasingly answer culturally situated questions whose surface form specifies a country but omits the subnational, community or situational context needed for a reliable answer. We study cultural answerability: whether the cultural scope supplied by a user supports a direct answer without presenting one situated practice as a country-wide norm. Using region-specific evidence from I N DICA , we construct a bilingual English–Hindi evaluation with 50 culturally stable and 50 culturally dependent held out questions. We evaluate two multilingual LLMs across three stages: explicit recognition of missing context, natural response policy, and paired regional counterfactual grounding. Models identify 89.5% of dependent conditions but correctly classify only 28.0% of stable conditions, revealing strong over detection of cultural dependence. Under natural assistance, the appropriate policy rate on dependent questions is 69.0%, while 30.5% of responses make an unscoped cultural commitment. Moreover in 28.0% of dependent model language conditions a model explicitly recognises missing context yet still makes such a commitment when answering naturally. Human adjudicated regional grounding succeeds in only 13.5% of paired conditions, showing that explicit regional context does not reliably produce grounded adaptation. These results show that cultural reliability is not primarily a knowledge problem: it is a calibration problem spanning recognition, response policy and grounded adaptation.</abstract>
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%0 Conference Proceedings
%T Country Is Not Enough: Recognition Action Gaps in Cultural Answerability
%A Channa, Aditya
%A Kapoor, Madhav
%A Singh, Jastej
%Y Ranaldi, Leonardo
%Y Pucci, Giulia
%Y Ranaldi, Federico
%Y Valentino, Marco
%Y Birch, Alexandra
%Y Zanzotto, Fabio Massimo
%S The Proceedings of the Workshop on Open Reasoning Across Cultures Languages (ORACLE)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-483-5
%F channa-etal-2026-country
%X Large language models increasingly answer culturally situated questions whose surface form specifies a country but omits the subnational, community or situational context needed for a reliable answer. We study cultural answerability: whether the cultural scope supplied by a user supports a direct answer without presenting one situated practice as a country-wide norm. Using region-specific evidence from I N DICA , we construct a bilingual English–Hindi evaluation with 50 culturally stable and 50 culturally dependent held out questions. We evaluate two multilingual LLMs across three stages: explicit recognition of missing context, natural response policy, and paired regional counterfactual grounding. Models identify 89.5% of dependent conditions but correctly classify only 28.0% of stable conditions, revealing strong over detection of cultural dependence. Under natural assistance, the appropriate policy rate on dependent questions is 69.0%, while 30.5% of responses make an unscoped cultural commitment. Moreover in 28.0% of dependent model language conditions a model explicitly recognises missing context yet still makes such a commitment when answering naturally. Human adjudicated regional grounding succeeds in only 13.5% of paired conditions, showing that explicit regional context does not reliably produce grounded adaptation. These results show that cultural reliability is not primarily a knowledge problem: it is a calibration problem spanning recognition, response policy and grounded adaptation.
%U https://aclanthology.org/2026.oracle-main.11/
%P 166-176

Markdown (Informal)

[Country Is Not Enough: Recognition Action Gaps in Cultural Answerability](https://aclanthology.org/2026.oracle-main.11/) (Channa et al., ORACLE 2026)

• Country Is Not Enough: Recognition Action Gaps in Cultural Answerability (Channa et al., ORACLE 2026)

ACL

• Aditya Channa, Madhav Kapoor, and Jastej Singh. 2026. Country Is Not Enough: Recognition Action Gaps in Cultural Answerability. In The Proceedings of the Workshop on Open Reasoning Across Cultures Languages (ORACLE), pages 166–176, Budapest, Hungary. Association for Computational Linguistics.