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AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic

Paper · 2025 · Nathaniel Romney Robinson, Shahd Abdelmoneim, Kelly Marchisio et al.

Summary

Large language models handle Modern Standard Arabic (MSA) well but tend to avoid the regional dialects (DA) that Arabic speakers actually use day to day. Many people are not fluent in MSA, so this gap can deepen social inequality. The field also lacked a working definition of what LLM proficiency in DA means. The authors propose AL-QASIDA, an evaluation suite that splits DA proficiency into four parts:

  • fidelity: producing the requested variety
  • understanding: following DA prompts
  • quality: fluency and accuracy of DA output
  • diglossia: translating between MSA and DA

They test nine LLMs on eight national varieties. Fidelity is measured with a new ADI2 score, which combines a dialect-identification classifier with a dialectness estimator. The main finding is that models understand DA better than they produce it. This is not because their DA is poor. They simply prefer to answer in MSA. Post-training seems to strengthen this preference, while few-shot examples counteract it. The suite and data are released so that future models can be measured the same way.

Key points

  • Fidelity uses ADI2, the product of two probabilities. The first is an ALDi dialectness score. The second is the probability of the target country, taken from the logits of the NADI 2024 baseline rather than its one-hot output.
  • A macro variant adds up NADI probabilities within one of four regions (Maghreb, Nile Valley, Levant, Gulf). This tolerates confusion between closely related neighbouring varieties.
  • The other dimensions use spBLEU for translation directions (DA↔English, MSA↔DA) and human judgments for instruction following and fluency.
  • Correlation analysis and decision trees regressed on ADI2 point to three main drivers: target variety, model choice and the number of few-shot examples. No input feature reached the strong-effect threshold of η² ≥ .14 or |ρ| > 0.5. Prompt dialectness correlated only weakly, with ρ ≈ .29 for ADI2 and ρ ≈ .31 for output dialectness.
  • The authors give practical recommendations:
    • GPT-4o for English-prompted requests in Egyptian or Moroccan.
    • Llama-3.1 for monolingual DA generation.
    • Five in-context examples raised ADI2 for Command-R+ across tasks and genres.

Interesting ideas

  • I1. Fidelity is scored as the product of two independent soft probabilities, "is this dialectal at all" and "is it the right dialect", rather than one hard label. This separates two different failure modes that a single classifier would merge.
  • I2. Scoring at region level avoids penalising outputs that are valid in several neighbouring varieties, a problem echoing earlier work that found most single-label "errors" were not errors.
  • I3. The evaluation separates what a model can do (understand, write fluently) from what it chooses to do (stay in MSA). This shows that a low score can reflect a default preference rather than missing competence.
  • I4. Base and post-trained versions of the same model are compared to find which training stage introduces a bias.
  • I5. Effect sizes and shallow decision trees are run over input features to see which ones actually drive failures, and an explicit "no strong correlate" counts as a result.

For Mova

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

  • I1 (learn): Silver trees from witness parsers could carry a factored confidence, for example agreement on the head times agreement on the label, so filtering can tell where disagreement comes from.
  • I2 (tree): When evaluating UD dialect conversion or parser disagreements, group analyses that are equally defensible into equivalence classes before computing LAS-style penalties.
  • I3 (back): Generation tests should judge register and style choice separately from grammaticality, so a fluent but wrongly formal output is diagnosed correctly.
  • I4 (learn): Silver data from different LLM teacher tiers could be compared in the same way, checking whether a tuned teacher skews the labels toward one register or construction type.
  • I5 (answer): Failed school-test or word-problem items could be regressed on features such as length, genre and construction, so the team targets real failure drivers instead of guesses.

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

🎓Johns Hopkins UniversityBaltimore · United States240 works · 96,487 citations

 Fields, topics and concepts

📚 Shelves 4The 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 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksSemitic languages · 2,908 works

Cite

Cite as Nathaniel Romney Robinson, Shahd Abdelmoneim, Kelly Marchisio and Sebastian Ruder. 2025. AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic. Findings of the Association for Computational Linguistics: ACL 2025.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.findings-acl.1137

@inproceedings{robinson2025al,
  title = {{AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic}},
  author = {Nathaniel Romney Robinson, Shahd Abdelmoneim, Kelly Marchisio and Sebastian Ruder},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  year = {2025},
  doi = {10.18653/v1/2025.findings-acl.1137},
  url = {https://doi.org/10.18653/v1/2025.findings-acl.1137}
}

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