🧑‍🔬 Hakim Hacid

🏛️ Affiliations

🔬Technology Innovation InstituteAbu Dhabi · United Arab Emirates2022–2026 · 59 paperscurrent🏢Alcatel-LucentBoulogne-Billancourt · France2009–2013 · 23 papers🎓Zayed UniversityAbu Dhabi · United Arab Emirates2015–2022 · 15 papers🎓Université Lumière Lyon 2Lyon · France2005–2011 · 13 papers🎓UNSW SydneySydney · Australia2008 · 3 papers🎓Université de LyonLyon · France2008 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance (2025 · 15 citations)
   Falcon-H1, from TII's Falcon LLM Team, is a family of open-weight LLMs that aims to get top-tier quality at much lower compute and memory cost.
2. Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps (2025 · 8 citations)
   Arabic LLM evaluation is spread across dozens of benchmarks that differ in how they were built, how large they are, and whether others can reproduce them.
3. Adapting Falcon3-7B Language Model for Arabic: Methods, Challenges, and Outcomes (2025 · 2 citations)
   Arabic remains poorly served by large language models. High-quality Arabic text is scarce, and many existing systems lean on machine-translated data, which tends to lose cultural and idiomatic meaning. The authors adapt the existing Falcon3-7B model to Arabic instead of training a new model from scratch. Their…
4. Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation (2026)
   Arabic LLM evaluation draws on many benchmarks, but they are uneven. Several are translated from English, many native sets were never carefully checked, coverage is scattered, and outputs are rarely published. QIMMA is a leaderboard that audits its sources before scoring any model. The authors chose 14 mostly native…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and PerformancearXiv:2507.22448Jingwei Zuo, Maksim Velikanov, Ilyas Chahed, et al.2025 · 15 citations
2. Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and GapsarXiv:2510.13430Ahmed Alzubaidi, Shaikha Alsuwaidi, Basma El Amel Boussaha, et al.2025 · 8 citations
3. 3LM: Bridging Arabic, STEM, and Code through Benchmarking ↗Proceedings of The Third Arabic Natural Language Processing Conference · no summary yetBasma El Amel Boussaha et al.2025 · 6 citations
4. Adapting Falcon3-7B Language Model for Arabic: Methods, Challenges, and OutcomesArabic Natural Language Processing Conference (ArabicNLP)Basma El Amel Boussaha, Mohammed Alyafeai, Ahmed Alzubaidi, et al.2025 · 2 citations
5. Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM EvaluationarXiv:2604.03395Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, et al.2026

🤝 Co-authors here

11 co-authors with a page, the most papers together first.

• Ahmed Alzubaidi ×3
• Basma El Amel Boussaha ×3
• Mohammed Alyafeai ×3
• Shaikha Alsuwaidi ×3
• Hamza Alobeidli ×2
• Leen AlQadi ×2
• Omar Alkaabi ×2
• Giulia Campesan
• Jingwei Zuo
• Maitha Alhammadi
• Mugariya Farooq

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Arabic Natural Language Processing Conference (ArabicNLP) ×2: ArabicNLP 2025

🔗 Cited by

Papers of our library cite their works 30 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated 31 October 2026

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