Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.S
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Abstract (arXiv)
Recent work has highlighted the culturally-contingent nature of commonsense knowledge. We introduce AMAMMERε, a test set of 525 multiple-choice questions designed to evaluate the commonsense knowledge of English LLMs, relative to the cultural contexts of Ghana and the United States. To create AMAMMERε, we select a set of multiple-choice questions (MCQs) from existing commonsense datasets and rewrite them in a multi-stage process involving surveys of Ghanaian and U.S. participants. In three rounds of surveys, participants from both pools are solicited to (1) write correct and incorrect answer choices, (2) rate individual answer choices on a 5-point Likert scale, and (3) select the best answer choice from the newly-constructed MCQ items, in a final validation step. By engaging participants at multiple stages, our procedure ensures that participant perspectives are incorporated both in the creation and validation of test items, resulting in high levels of agreement within each pool. We evaluate several off-the-shelf English LLMs on AMAMMERε. Uniformly, models prefer answers choices that align with the preferences of U.S. annotators over Ghanaian annotators. Additionally, when test items specify a cultural context (Ghana or the U.S.), models exhibit some ability to adapt, but performance is consistently better in U.S. contexts than Ghanaian. As large resources are devoted to the advancement of English LLMs, our findings underscore the need for culturally adaptable models and evaluations to meet the needs of diverse English-speaking populations around the world.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 21 Oct 2024.
Builds on
21 of 36 references.
References · 21
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- 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
- 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
- 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 486
- 2018SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inferencecited by 333
- 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 330
- 2018Hypothesis Only Baselines in Natural Language Inferencecited by 291
- 2019Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoningcited by 281
- 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
9 more
- 2024CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologiescited by 98
- 2024Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?cited by 68
- 2021Broaden the Vision: Geo-Diverse Visual Commonsense Reasoningcited by 59
- 2024CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Promptingcited by 55
- 2024Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsensecited by 50
- 2016Ordinal Common-sense Inferencecited by 35
- 2017Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scalingcited by 23
- 2023Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Featurescited by 18
- 2024Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoningcited by 13
Built on by
Citing papers per year: 2025: 5; 2026: 4.
9 papers.
- 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Abovecited by 51
- 2026Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulationscited by 5
- 2025On the Mutual Influence of Gender and Occupation in LLM Representationscited by 4
- 2026Common to Whom? Regional Cultural Commonsense and LLM Bias in Indiacited by 3
- 2026Structuring the Space of Sociotechnical Alignmentcited by 2
- 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactionscited by 1
- 2025Everyday Physics in Korean Contexts: A Culturally Grounded Physical Reasoning Benchmarkcited by 1
- 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibilitycited by 1
- 2026CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systemscited by 0
- 2026CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems18 Sep 2026
- 2026Structuring the Space of Sociotechnical Alignment2 Jun 2026
- 2026Common to Whom? Regional Cultural Commonsense and LLM Bias in India22 Jan 2026
- 2026Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations15 Jan 2026
- 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility9 Oct 2025
- 2025Everyday Physics in Korean Contexts: A Culturally Grounded Physical Reasoning Benchmark22 Sep 2025
- 2025On the Mutual Influence of Gender and Occupation in LLM Representations9 Mar 2025
- 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactions25 Feb 2025
- 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above19 Feb 2025