Papers by Christabel Acquaye
Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the US (2024.emnlp-main)
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| Challenge: | Recent work has highlighted the culturally-contingent nature of commonsense knowledge . a multi-stage process is used to evaluate the commonsence of English LLMs . |
| Approach: | They propose a test set of 525 multiple-choice questions to evaluate commonsense knowledge of English LLMs in Ghana and the u.s. They use existing commonsensible datasets to rewrite them in a multi-stage process. |
| Outcome: | The proposed model improves on the culturally-contingent commonsense knowledge of English LLMs in Ghana and the United States. |
My LLM might Mimic AAE - But When Should It? (2025.naacl-long)
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| Challenge: | a study examines the representation of African American English in large language models . a survey of black americans and annotation of LLM outputs shows that Black Americans prefer to use AAE in formal settings . |
| Approach: | They examine Black Americans' perceptions of how effective AI tools are at producing authentic African American English in large language models. |
| Outcome: | The results show that Black Americans prefer to use LLMs in formal settings over informal ones . the results show they prefer to produce AAE in less formal settings . |
Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations (2026.findings-acl)
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| Challenge: | Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. |
| Approach: | They propose to use large language models to model difficulty of multiple-choice math questions for real-world students. |
| Outcome: | The proposed model predicts difficulty of multiple-choice math questions for students . correlations between model and real-world difficulty are high, the authors show . |
Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? (2024.acl-short)
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| Challenge: | We study whether large language models exhibit race- and gender-based name discrimination in hiring decisions . |
| Approach: | They propose templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. |
| Outcome: | The proposed model generates an acceptance or rejection email based on the applicant's first name . |