Papers by Saeed Almheiri
Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World (2025.findings-emnlp)
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| 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. |
Commonsense Reasoning in Arab Culture (2025.acl-long)
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Abdelrahman Sadallah, Junior Cedric Tonga, Khalid Almubarak, Saeed Almheiri, Farah Atif, Chatrine Qwaider, Karima Kadaoui, Sara Shatnawi, Yaser Alesh, Fajri Koto
| Challenge: | Existing studies on commonsense reasoning in Arabic have relied on machine translations that lack cultural depth and introduce anglocentric biases. |
| Approach: | They propose a commonsense reasoning dataset in Arabic that covers 13 Arab countries. |
| Outcome: | The proposed dataset covers 13 countries across the Gulf, Levant, North Africa, and the Nile Valley. |
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)
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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou
| Challenge: | Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support. |
| Approach: | They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims . |
| Outcome: | The proposed benchmark evaluates behavioral biases of large language models across economic scenarios. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
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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. |
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)
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Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
| Challenge: | Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues. |
| Approach: | They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. |
| Outcome: | The proposed model performs worse on all three tasks than the MSA benchmark. |