@inproceedings{jamshidi-etal-2026-framenet,
title = "{F}rame{N}et-Cultures: A Benchmark for Evaluating {LLM}s via Cross-Cultural Frame Semantics",
author = "Jamshidi, Neda and
S{\o}gaard, Anders and
Bianchini, Monica",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.491/",
pages = "10090--10131",
ISBN = "979-8-89176-395-1",
abstract = "Large language models (LLMs) exhibit cultural biases, yet existing benchmarks rely on closed-form, domain-specific questionnaires. We introduce FRAMENET-CULTURES, a benchmark for evaluating cultural alignment in LLMs based on Fillmore-style frame semantics. Using the EveryCulture encyclopedia, we construct a lexicon of 18 cultural frames (e.g., greeting,child-rearing) across 20 countries, treating it as a structured reference for comparison rather than a definitive representation of contemporary societies. For each frame, we prompt five major LLMs{---}ChatGPT-5, Gemini-2.5-Flash, Mistral-Large, Qwen-3-Max, DeepSeek-V3.2{---}three times to generate open-ended instantiations, which are manually annotated and binarized. We measure alignment with country- and continent-level profiles using normalized Hamming distance, and validate cultural recognizability through human evaluation of generated dialogues. Under culture-neutral prompting, outputs align most closely with European profiles, followed by Asian and American ones, indicating a consistent cross-model pattern. With culture-specific prompting, models shift toward the target regions, aligning most strongly with Africa for Ethiopia and with Asia for India. FRAMENET-CULTURES is the first open-ended benchmark for cultural alignment relying on frame semantics. Data, prompts, and annotations are publicly available at https://github.com/neda-jamshidi/FrameNet-Cultures."
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<abstract>Large language models (LLMs) exhibit cultural biases, yet existing benchmarks rely on closed-form, domain-specific questionnaires. We introduce FRAMENET-CULTURES, a benchmark for evaluating cultural alignment in LLMs based on Fillmore-style frame semantics. Using the EveryCulture encyclopedia, we construct a lexicon of 18 cultural frames (e.g., greeting,child-rearing) across 20 countries, treating it as a structured reference for comparison rather than a definitive representation of contemporary societies. For each frame, we prompt five major LLMs—ChatGPT-5, Gemini-2.5-Flash, Mistral-Large, Qwen-3-Max, DeepSeek-V3.2—three times to generate open-ended instantiations, which are manually annotated and binarized. We measure alignment with country- and continent-level profiles using normalized Hamming distance, and validate cultural recognizability through human evaluation of generated dialogues. Under culture-neutral prompting, outputs align most closely with European profiles, followed by Asian and American ones, indicating a consistent cross-model pattern. With culture-specific prompting, models shift toward the target regions, aligning most strongly with Africa for Ethiopia and with Asia for India. FRAMENET-CULTURES is the first open-ended benchmark for cultural alignment relying on frame semantics. Data, prompts, and annotations are publicly available at https://github.com/neda-jamshidi/FrameNet-Cultures.</abstract>
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%0 Conference Proceedings
%T FrameNet-Cultures: A Benchmark for Evaluating LLMs via Cross-Cultural Frame Semantics
%A Jamshidi, Neda
%A Søgaard, Anders
%A Bianchini, Monica
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jamshidi-etal-2026-framenet
%X Large language models (LLMs) exhibit cultural biases, yet existing benchmarks rely on closed-form, domain-specific questionnaires. We introduce FRAMENET-CULTURES, a benchmark for evaluating cultural alignment in LLMs based on Fillmore-style frame semantics. Using the EveryCulture encyclopedia, we construct a lexicon of 18 cultural frames (e.g., greeting,child-rearing) across 20 countries, treating it as a structured reference for comparison rather than a definitive representation of contemporary societies. For each frame, we prompt five major LLMs—ChatGPT-5, Gemini-2.5-Flash, Mistral-Large, Qwen-3-Max, DeepSeek-V3.2—three times to generate open-ended instantiations, which are manually annotated and binarized. We measure alignment with country- and continent-level profiles using normalized Hamming distance, and validate cultural recognizability through human evaluation of generated dialogues. Under culture-neutral prompting, outputs align most closely with European profiles, followed by Asian and American ones, indicating a consistent cross-model pattern. With culture-specific prompting, models shift toward the target regions, aligning most strongly with Africa for Ethiopia and with Asia for India. FRAMENET-CULTURES is the first open-ended benchmark for cultural alignment relying on frame semantics. Data, prompts, and annotations are publicly available at https://github.com/neda-jamshidi/FrameNet-Cultures.
%U https://aclanthology.org/2026.findings-acl.491/
%P 10090-10131
Markdown (Informal)
[FrameNet-Cultures: A Benchmark for Evaluating LLMs via Cross-Cultural Frame Semantics](https://aclanthology.org/2026.findings-acl.491/) (Jamshidi et al., Findings 2026)
ACL