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BALSAM: A Platform for Benchmarking Arabic Large Language Models

Paper · 2025 · Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed et al.

Summary

Arabic LLMs still lag behind English ones, and the gap is hard to measure. Most Arabic benchmarks use static public data and cover only a narrow set of capabilities. They also lack a hosted platform with hidden test sets, so training-data contamination is likely and progress is difficult to track. A large consortium of Gulf and other institutions responds with BALSAM, a community-run benchmark paired with an evaluation platform and a public leaderboard. It collects 78 tasks in 14 categories, ranging from summarization, QA and translation to logic, program execution and sequence tagging. In total it has roughly 52K examples, and its test portion stays blind. The authors also check automatic scoring methods against human ratings. An LLM-as-a-judge agrees with people far better than BLEU, ROUGE or BERTScore. Using the judge on 22 models, large general systems such as GPT-4o and Gemini 2.0 Flash rank highest, ahead of smaller Arabic-focused models like Jais and Fanar. Some big models score poorly, so size alone does not explain quality. The resource offers shared, contamination-resistant infrastructure for Arabic evaluation.

Key points

  • BALSAM has 78 tasks in 14 categories and about 52K items, split into roughly 37K blind test and 15K development examples. It is hosted at benchmarks.ksaa.gov.sa with a leaderboard.
  • Averaged over categories, correlation with human judgments is 0.918 for LLM-as-a-Judge. Lexical and embedding metrics score far lower: ROUGE-LSum 0.111, BLEU 0.134 and BERTScore 0.184. Their "extracted" variants rise only to 0.326, 0.186 and 0.346.
  • On some categories the overlap metrics even correlate negatively with humans. All of them are negative for Creative Writing, and extracted ROUGE reaches −0.754 on Summarization.
  • The leaderboard top is GPT-4o (average 2.05; 2.03 without translation), followed by Gemini 2.0 Flash, Iron Horse and DeepSeek V3. Mistral large, DBRX-instruct and Aragpt2-mega trail.
  • The authors list several limitations:
    • Some models could not finish certain categories because of context-length limits.
    • Translated datasets brought in cultural and terminological mismatches, for example in religious phrasing.
    • Fluency, hallucination, multi-turn chat and tool use are left for a future iteration.

Interesting ideas

  • I1. Keep the test split hidden behind a submission platform and publish only the dev split, which makes leakage into training data much harder and keeps scores comparable over time.
  • I2. Before trusting a metric, check it against human ratings separately per task category, since a metric can work for translation yet be negatively correlated for creative writing or summarization.
  • I3. Report per-category scores, not just one average, because models with similar totals show very different profiles (strong in summarization but weak in sequence tagging).
  • I4. Treat translated evaluation data as a possible source of bias, since translation can carry over wording that is unnatural or culturally off for the target audience.
  • I5. The authors conclude that scale alone fails to predict quality and point to tokenization, amount of in-language data and in-language fine-tuning as likely drivers.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Mova could hold a sealed held-out set of texts and questions that no training, repair-rule induction or self-play loop may touch, used only for periodic checks.
  • I2 (back): Before using BLEU/ROUGE-style overlap to score the text → graph → text compressor, Mova should check that it tracks human judgments of meaning preservation; otherwise it should use a judge-based or graph-level comparison instead.
  • I3 (answer): Results on school tests, word problems and fable QA could be broken down by question type, showing which pipeline stage tends to fail behind each weakness.
  • I4 (answer): When Mova's tale or test sets come from translations, a small review pass could flag odd phrasing before questions are scored.
  • I5 (tokens): This supports investing in tokenizer and morphology quality for English text rather than assuming a bigger parser model will compensate for upstream segmentation errors.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓New York UniversityNew York · United States201 works · 100,180 citations🎓University of WaterlooWaterloo · Canada67 works · 13,592 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates122 works · 4,602 citations🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates124 works · 5,676 citations🎓Qatar UniversityDoha · Qatar48 works · 1,026 citations🎓King Abdullah University of Science and TechnologyJeddah · Saudi Arabia27 works · 1,084 citations🎓King Saud UniversityRiyadh · Saudi Arabia37 works · 527 citations🎓King Abdulaziz UniversityJeddah · Saudi Arabia31 works · 508 citations🎓Prince Sattam Bin Abdulaziz UniversityAl Kharj · Saudi Arabia17 works · 489 citations🎓University of BishaBisha · Saudi Arabia4 works · 56 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksEducation · 203 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed, Waad Alshammari, Muneera Alhoshan, Amal Almazrua, Asma Al Wazrah, Mais Alheraki, Firoj Alam, Preslav Nakov, Norah Alzahrani, Eman AlBilali, Nizar Habash, Abdelrahman El-Sheikh, Muhammad Elmallah, Haonan Li, Hamdy Mubarak, Mohamed Anwar, Zaid Alyafeai, Ahmed Abdelali, Nora Altwairesh, Maram Hasanain, Abdulmohsen Al Thubaity, Shady Shehata, Bashar Alhafni, Injy Hamed, Go Inoue, Khalid Elmadani, Ossama Obeid, Fatima Haouari, Tamer Elsayed, Emad Alghamdi, Khalid Almubarak, Saied Alshahrani, Ola Aljarrah, Safa Alajlan, Areej Alshaqarawi, Maryam Alshihri, Sultana Alghurabi, Atikah Alzeghayer, Afrah Altamimi, Abdullah Alfaifi and Abdulrahman AlOsaimy. 2025. BALSAM: A Platform for Benchmarking Arabic Large Language Models. Proceedings of The Third Arabic Natural Language Processing Conference.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.arabicnlp-main.21

@inproceedings{almatham2025balsam,
  title = {{BALSAM: A Platform for Benchmarking Arabic Large Language Models}},
  author = {Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed, Waad Alshammari, Muneera Alhoshan, Amal Almazrua, Asma {Al Wazrah}, Mais Alheraki, Firoj Alam, Preslav Nakov, Norah Alzahrani, Eman AlBilali, Nizar Habash, Abdelrahman El-Sheikh, Muhammad Elmallah, Haonan Li, Hamdy Mubarak, Mohamed Anwar, Zaid Alyafeai, Ahmed Abdelali, Nora Altwairesh, Maram Hasanain, Abdulmohsen {Al Thubaity}, Shady Shehata, Bashar Alhafni, Injy Hamed, Go Inoue, Khalid Elmadani, Ossama Obeid, Fatima Haouari, Tamer Elsayed, Emad Alghamdi, Khalid Almubarak, Saied Alshahrani, Ola Aljarrah, Safa Alajlan, Areej Alshaqarawi, Maryam Alshihri, Sultana Alghurabi, Atikah Alzeghayer, Afrah Altamimi, Abdullah Alfaifi and Abdulrahman AlOsaimy},
  booktitle = {Proceedings of The Third Arabic Natural Language Processing Conference},
  year = {2025},
  doi = {10.18653/v1/2025.arabicnlp-main.21},
  url = {https://doi.org/10.18653/v1/2025.arabicnlp-main.21}
}

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