Papers by Saied Alshahrani

3 papers
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

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Challenge: In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach: They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome: The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.
Arabic Synonym BERT-based Adversarial Examples for Text Classification (2024.eacl-srw)

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Challenge: Often, research studies quantifying the impact of adversarial text attacks have been applied only to models trained in English.
Approach: They propose a word-level study of adversarial text examples in Arabic . they use a synonym attack with a BERT model to assess their robustness .
Outcome: The proposed attack compares Arabic adversarial examples with their original examples and regains 2% accuracy after training.
CIDAR: Culturally Relevant Instruction Dataset For Arabic (2024.findings-acl)

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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.

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