Papers by Mena Attia
Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study examined the potential for cross-cultural transfer of commonsense reasoning . merely 12 culture-specific examples from one country can improve performance in others by 10% on average . |
| Approach: | They evaluate cross-cultural transfer of commonsense reasoning within the arab world . they use in-context learning and demonstration-based reinforcement to evaluate alignment methods . |
| Outcome: | The proposed model can improve performance in cultures with cultural similarities in the Arab world by 10% on average. |
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs’ Cultural Processing of Figurative Language (2026.eacl-long)
Copied to clipboard
| Challenge: | Using figurative language as a proxy for cultural nuance and local knowledge, large language models struggle with connotative meaning. |
| Approach: | They evaluate large language models' ability to process culturally grounded language . they use figurative language as a proxy for cultural nuance and local knowledge . |
| Outcome: | The proposed models can understand and use figurative expressions that encode local knowledge and social nuance. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
Copied to clipboard
Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |