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Amr Mohamed

Amr Mohamed has 5 papers in our library, in 2025–2026, most of them on Language models & pretraining, Semitic languages, Low-resource languages; often with Guokan Shang, Michalis Vazirgiannis, Hadi Abdine.

5 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 43 · 428 works · 9,652 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 37 times in our library · ⭐ Paper Stars ranking: #8088

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 15 more institutions 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. Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect (2025 · 18 citations)
    Arabic-focused LLMs such as Jais, AceGPT and ALLaM mostly balance English and Modern Standard Arabic and largely ignore spoken dialects.
  2. Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts (2025 · 9 citations)
    Egyptian Arabic is often written in two ways: in Arabic script and in a Latin-based romanization called Arabizi or Franco-Arabic.
  3. Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning (2026 · 7 citations)
    Large language models are usually pretrained on documents fed in random order. This leaves open whether arranging data from simple to complex could save compute. The authors run what they present as the first systematic, large-scale test of curriculum learning for LLM pretraining. They use an English subset of…
  4. Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text (2026 · 6 citations)
    The paper asks whether large language models still comprehend and reason well when a text mixes languages (code-switching, CSW).
  5. Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules (2026)
    Diffusion large language models (dLLMs) can generate text in parallel and attend in both directions, but they are slow at inference.

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 37 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

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