Papers by Muhammad ElNokrashy

2 papers
Investigating Cultural Alignment of Large Language Models (2024.acl-long)

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Challenge: Large Language Models (LLMs) are used to represent the diversity of human experience and culturally sensitive topics.
Approach: They propose a method leveraging anthropological reasoning to enhance cultural alignment by prompting LLMs with different pretraining data mixtures in Arabic and English.
Outcome: The proposed method enables users to better represent the diversity of human experience and the plurality of different cultures.
Depth-Wise Attention (DWAtt): A Layer Fusion Method for Data-Efficient Classification (2024.lrec-main)

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Challenge: Language Models pretrained on large textual data can encode different types of knowledge simultaneously.
Approach: They propose a method to re-surface intermediate layer features from non-final layers by combining them with a concatenation-based layer fusion method.
Outcome: The proposed method outperforms the baseline model on large datasets and shows 3.68 9.73% gain.

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