Papers by Deema Alnuhait
FACTCHECKMATE: Preemptively Detecting and Mitigating Hallucinations in LMs (2025.findings-emnlp)
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| Challenge: | Language models (LMs) hallucinate. |
| Approach: | They introduce a classifier that predicts whether LMs hallucinate based on model’s hidden states before decoding begins. |
| Outcome: | The proposed model preemptively detects hallucinations by learning a classifier that predicts whether the LM will hallucinate . if a hallucinomy is detected, FactCheckmate intervenes by adjusting the model’s hidden states to produce more factual outputs. |
CIDAR: Culturally Relevant Instruction Dataset For Arabic (2024.findings-acl)
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Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait, Saied Alshahrani, Gubran Abdulrahman, Gamil Ahmed, Qais Gawah, Zead Saleh, Mustafa Ghaleb, Yousef Ali, Maged Al-shaibani
| Challenge: | Instruction tuning datasets predominantly cater to English or are derived from English-dominated LLMs. |
| Approach: | They propose to use an Arabic instruction tuning dataset culturally aligned by native Arabic speakers to address drawbacks of finetuning LLMs on machine-generated or machinetranslated datasets. |
| Outcome: | The proposed datasets show that they achieve better cultural alignment than models fine-tuned on other datasets. |
AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic (2025.coling-main)
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| Challenge: | Existing benchmarks for large language models (LLMs) in Arabic are lacking . despite progress in their development, there is a lack of comprehensive trustworthiness evaluation benchmarks . |
| Approach: | They propose to use Arabic as a language to assess trustworthiness of large language models. |
| Outcome: | The proposed benchmark measures the trustworthiness of large language models in Arabic. |