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Preventing incorrect or culturally inappropriate Arabic answers means running an agent that ties factual claims to sources a reviewer can check, says when the evidence is weak or in conflict, and is judged by people who know the Arabic variety and the community being served. Factuality, source support, faithfulness, safety, dialect fit, and cultural appropriateness are separate qualities. A strong general-language score leaves local reliability unresolved. Name the users, region, variety, register, channel, and the cost of a wrong answer. Ground time-sensitive facts and verify each citation. Test local prompts, including dialect and safety cases. Score each failure type separately. Use reviewers who know that community. Keep measuring after launch: a pre-deployment result covers one model version and one test set. NIST AI 600-1 requires source and citation checks before deployment and during monitoring.
What do I need in place before I change the agent?
Start with the materials that make an Arabic failure diagnosable. You need a written deployment context, a source collection you are allowed to cite, reviewers who know the target variety and the task, a log that stores error types separately, and a named escalation path for high-impact answers. You also need access to the system prompt, the retrieval index, and the citation display, because those three pieces decide whether a fluent paragraph is actually tied to evidence.
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Write the context down before you collect prompts. Record the region or community, the Arabic variety or varieties people will use, the formal or conversational register, whether the interaction is written or spoken, the script and spelling conventions you will accept, and what a wrong answer would cost. A grammar mistake in a language-learning demo and a wrong eligibility date in a public-service agent are different risks, and they need different release rules.
If any of those pieces is missing, later scores collapse into a single impression of “Arabic quality.” That impression hides the failure you most need to see: a claim with no source, a source the answer distorts, a citation that points at the wrong sentence, a dialect the model misread, or a tone that is disrespectful in the community you serve.
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Treat each model version, prompt version, and retrieval-index version as a new system. A result you recorded for an earlier build describes that build. Store the version identifiers beside every score so a later reader can tell what was measured.
How do I define who the Arabic agent is for?
Name the users and the job first. Arabic varieties differ in vocabulary, grammar, and social meaning, so a result reported for Arabic as a whole, or for Modern Standard Arabic alone, leaves performance on the variety your users actually speak unresolved. AraDiCE, a COLING 2025 benchmark suite for dialectal and cultural capabilities, exists because those capabilities have to be measured on their own. Towards Responsible Natural Language Annotation for the Varieties of Arabic makes the matching point about data work: cultural and sociolinguistic nuance belongs in the design of Arabic annotation, which includes how you describe your own test items.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFor a single deployment, specify all of the following:
- Community and region. Country, city, or diaspora community, plus the institution the agent represents.
- Variety. Modern Standard Arabic, a regional variety such as Egyptian, Levantine, Gulf, or Maghrebi Arabic, or an explicit mix. If you serve more than one, each variety gets its own test slice.
- Register. News prose, classroom explanation, customer-service chat, or another level of formality you can describe with examples.
- Channel. Typed Arabic, Latin-letter transliteration, or speech. For speech, judge the transcript the model actually received, because a recognition error changes the Arabic string before the agent answers.
- Script and spelling. Arabic script, common orthographic variants, and transliteration that uses Latin letters and numerals.
- Task. Question answering, summarization, form-filling, moderation, or multi-step assistance. Findings from one task stay attached to that task.
- Harm threshold. Which topics may be answered automatically, which require a caveat, and which must go to a person.
Pooling every Arabic-speaking user into one test group will track whichever slice is largest. A pass on formal written prompts can sit on top of a systematic miss in the conversational variety that dominates real traffic. When the product cannot tell communities apart at runtime, the unmet requirement in any community you intend to serve holds the release. When routing by context is reliable, a community whose slice passes can ship while another slice stays behind a handoff.
Match the task boundary to the evidence you will cite later. AraHalluEval studies generative question answering and summarization. Its hallucination findings are about those tasks and the systems it evaluated. They are a reason to inspect those behaviors carefully. They are not a measured error rate for every Arabic agent, every country, or every future model release.
How do I ground facts and make uncertainty visible?
For factual or time-sensitive answers, retrieve material that covers the user’s place and time, and keep every factual sentence tied to a span a reviewer can open. Then check that the span supports that sentence. A citation-shaped string, a bare URL, or a list of links under a paragraph does not show support. The official instruction in the NIST Generative AI Profile (NIST AI 600-1, 2024) is direct: “Review and verify sources and citations in GAI system outputs during pre-deployment risk measurement and ongoing monitoring activities.”
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Use this check on each answer before a factual claim is shown:
- Split the draft into claims. Mark each one as factual, tonal or cultural, or purely conversational.
- For every factual claim, attach a retrieved span. If no span was retrieved, the claim stays unsupported.
- Read the span against the claim. Check the date, the place, the institution, and any condition such as age, residency, or eligibility.
- Reject a span about a different region or an older rule when the user asked about their own current situation.
- If two credible spans conflict, show the conflict or abstain. Do not blend them into one confident sentence.
- If the sources are silent on the dialect term or the local institution in the question, say what you could not verify. Ask a clarifying question or route the case to review.
- Display a citation only after it has been matched to the adjacent claim. Log the span, the claim, the document identifier, and the date of the check together.
Uncertainty has to be legible to the user. A usable uncertainty statement names the limit, names what would resolve it, and offers a next step. In Modern Standard Arabic, a line such as “لم أتحقق من هذا في مصدر حالي خاص بمنطقتك” tells the user the gap is local evidence, which is a different message from a polished guess. The same idea should be available in the register your deployment spec chose. A clarification question is appropriate when the missing piece is something the user knows, such as their city or the document they mean. A handoff is appropriate when the missing piece is a source you should already have held, or when the topic is on the high-impact list.
Factuality and faithfulness are different checks. Factuality asks whether the claim is supported and true for the stated context. Faithfulness asks whether the answer adds, drops, or changes what the source said, including a summary that keeps the words and loses a condition. AraHalluEval, by Alansari and Luqman (ArabicNLP 2025), evaluates both. The authors use 12 fine-grained hallucination indicators and report results for 12 LLMs, covering Arabic and multilingual systems, on generative question answering and summarization. Across those models and tasks, factual hallucinations were more prevalent than faithfulness errors. Quote that comparison only for that study. It supplies a reason to log both error families. It does not establish a worldwide percentage of Arabic agent errors.
Read the paper’s 12 indicators when you design your error log, and map them onto your domain. Keep the paper’s categories intact when you cite the paper. Add domain-specific labels, such as a wrong fee, a wrong school term, or a misstated eligibility rule, beside them. An internal label set can be shorter, as long as factual invention and source distortion remain separate fields.
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Here is the same question handled two ways. The example is an illustration of the check, not a measured case. A user asks when school registration closes in their city. An ungrounded answer gives a fluent date and a ministry name with nothing logged behind the sentence. A grounded answer cites the education authority’s current page, states the deadline that page prints, states the date the page was retrieved, and, if the page is silent about a particular district, says so and asks which district applies. The second answer can still be wrong if the page is outdated. That is why the check includes the document date, and why monitoring repeats it.
Apply the same discipline to text the user never sees as a “final answer.” If one step summarizes retrieved pages for a later step, that summary is a generative summary and can drop a condition or add a cause. Verify the intermediate summary against the spans before a downstream step treats it as fact. A multi-step agent can pass a spot check on the last paragraph while an earlier unverified claim has already chosen the wrong tool or the wrong form.
How do I build a test set for dialect, culture, and safety?
A fluency or grammar set will not show whether the agent is safe, faithful, or appropriate for the community in the deployment spec. AraSafe (Findings of EMNLP 2025) benchmarks safety in Arabic LLMs and reports safety blind spots for the models it studied. AraDiCE addresses dialectal and cultural capabilities directly. Build the test set from the communities you named, and have qualified reviewers create or assess the items.
Include all of the following, each labeled with the variety it represents and how it was obtained:
- Prompts in each target variety, plus Modern Standard Arabic where the product also receives formal writing.
- Code-switching in the language mix your users actually type or speak.
- Spelling variation and transliteration, including Latin-letter Arabic.
- Expressions that are ambiguous across regions, so a single glossary definition is not enough.
- Local personal names, place names, institutions, holidays, and forms of address.
- Culturally sensitive subjects that are realistic for this product, scored for stereotyping, disrespect, and misplaced assumptions.
- Ordinary low-risk questions, so over-refusal is visible.
- Adversarial and safety-relevant prompts written in the target language and variety, not only translated from an English red-team list at the end of the project.
AraSafe’s published study evaluates four Arabic-centric chat models and three multilingual chat models. That is a description of the study. It does not cover every commercial model or every Arabic-speaking community. Use it to justify Arabic safety items of your own, then fill those items with situations your deployment can actually encounter. Idioms, dialectal insults, religious and political references, and local institutions often have no English original, so a translated prompt file leaves those cases unmeasured.
Each item needs an expected behavior, not only a prompt. For a factual item, list the sources that may be cited and the claims that would be unsupported. For a sensitive item, state whether the agent should answer with constraints, ask a question, abstain, or hand off to a person. For a dialect item, state the incoming variety and the reply variety your spec allows. A single reference answer written only in formal Arabic will punish a correct colloquial reply that your own spec requested.
The 2022 ACL findings paper on responsible annotation is the documentation standard for this set. Record the variety, the cultural context the label assumes, and who was qualified to judge it. A prompt copied from a public webpage still needs a reviewer who can say which community it represents. Scraped text repeats whatever errors and stereotypes were already online; it does not arrive pre-reviewed.
Keep a held-out slice that prompt authors do not retune on. When a live failure appears, add that pattern to a regression slice and leave the held-out slice intact, so the next release has at least one set of items the team did not optimize against item by item.
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Report separate qualities. An average across all Arabic prompts follows the biggest slice of the set and can conceal a severe failure in one variety or one high-risk use. The NIST AI RMF Playbook’s Measure guidance calls for measurement that matches the risk, including human review of unexpected inputs and of whether generated outputs are reliable, and for comparison between production behavior and pre-deployment results.
Score at least these dimensions. The rubric is an operational checklist synthesized from the concerns in AraHalluEval, AraSafe, AraDiCE, AraTrust, and NIST measurement guidance. No one of those publications publishes this combined table as its official score.
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| Dimension | What to examine | What to store |
|---|---|---|
| Factual correctness and source support | Whether the claim is supported by the retrieved sources for this place and time | Claim, span, verdict, date checked |
| Faithfulness | Additions, omissions, and altered meaning relative to the source, including dropped conditions | Source span beside the answer span |
| Citation accuracy | Whether the displayed citation exists, is relevant, and is attached to the right sentence | Document identifier, URL if shown, date checked |
| Dialect and register | Whether the user’s variety was understood and whether the reply variety matches the spec | Expected variety, produced variety |
| Cultural appropriateness | Stereotypes, disrespect, wrong assumptions, and tone that misfires for the community | Reviewer notes and the community judged |
| Safety | Harmful requests, unsafe advice, and failures to refuse or constrain | Harm category and action taken |
| Uncertainty behavior | Whether the agent clarified, abstained, or escalated where the spec requires it | Expected action beside actual action |
AraTrust (COLING 2025) evaluates trustworthiness of LLMs in Arabic as its own problem. Keep a trustworthiness line in internal reports so smooth prose cannot stand in for reliability. Safety and culture overlap in real answers and still need separate rows. A reply can avoid prohibited content and still talk down to a community. A reply can sound culturally familiar and still give dangerous instructions. A safety pass does not close the cultural review, and a warm tone does not close the factual review.
Break every report out by variety and by task. Publish coverage beside accuracy. A system that abstains more often can look stronger if you grade only the answers it was willing to give. Refusal quality belongs in the same report: a specific, respectful refusal is a different outcome from a generic block that ignores the question, and from a refusal on an ordinary low-risk prompt.
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How do I review answers with people who know the community?
Use Arabic-speaking evaluators who know the intended community and the use case. Ask them to judge meaning, respectfulness, safety, and appropriateness, along with grammar. Fluency in formal written Arabic is relevant and still leaves a regional vernacular, and that community’s standards of respect, unexamined. Give reviewers written examples of acceptable and unacceptable answers and a rubric that stays stable across the round.
On sensitive items, collect independent judgments before discussion. Record disagreements with the reason and with the variety each reviewer represents. An average that erases the disagreement will hide a phrase one community reads as neutral and another reads as dismissive. That difference is a finding about the agent. NIST’s measurement guidance asks teams to document the contribution of human domain knowledge, which includes who reviewed, which context they know, and where they did not agree.
For high-impact answers, set escalation criteria before launch. Name the role that may approve, edit, or block a reply, the time allowed for that decision, and the message the user sees while the case waits. The responsible role has to be able to act. A model provider’s general documentation does not appoint a local approver for a medical, legal, financial, or child-safety answer.
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Outside review can help when your team does not include the target variety. The qualification that matters is familiarity with that dialect and with the task, for example customer support, clinical intake, or classroom use. General Arabic translation and dialect-aware safety review are different assignments. Ask the reviewers to cover at least these questions:
- Did the agent understand the user’s variety, including idioms and local names?
- Does the reply’s variety and politeness fit the situation in the spec?
- Is a cultural assumption doing unsupported work, such as an assumption about family structure, religion, gender, region, or politics?
- Did humor, idiom, or religious language land as the reviewer expects for that community?
- Was a refusal specific and respectful, or a generic blockage?
Schedule that review as part of the release, with paid time for adjudication on sensitive categories. AraDiCE and the responsible-annotation literature treat dialect and cultural judgment as distinct work. Leaving it to whoever is free on launch day produces a thin sample and no record of disagreement.
Review a current sample after every material change: model swap, index rebuild, system-prompt edit, or safety-rule edit. An earlier sign-off describes the previous bundle of versions.
How do I monitor the agent after it is live?
Pre-deployment numbers describe the test set you had, on that date, for that version. The NIST Measure guidance recommends lifecycle measurement: compare deployed behavior with pre-deployment metrics, review unexpected inputs and unreliable outputs, and evaluate output quality against new ground truth when it becomes available.
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After launch, run a standing loop:
- Sample live outputs and take in user error reports. Over-sample high-impact topics and any variety that was thin in the original set. A uniform sample of all chats will mostly reflect easy greetings if that is what people send.
- Where it is appropriate and lawful, separate metrics by dialect or target context. State the purpose, limit retention, and avoid building a covert profile of how a person speaks in order to label them.
- Compare each live error type with the matching pre-deployment metric. Investigate gaps rather than waiting for the average to move.
- Inspect citation failures on a schedule and whenever a source owner publishes a correction. A link that still opens can point at a superseded rule or a story that was later withdrawn.
- When a regulation, a public-health page, or a reviewer adjudication supplies new ground truth, rescore recent answers against it.
- Update the source index and the test set when facts or local usage change. Retire superseded documents so they cannot be retrieved by mistake, and keep the retirement date in the source log.
- Add the new input pattern to the regression slice, note the variety, and decide whether the fix is a source update, a prompt constraint, a refusal rule, or a human handoff.
Re-check the other varieties after a patch aimed at one community. A stricter citation rule can lift factual support and also increase refusals. A dialect-specific phrasing fix can misfire in a neighboring variety. Both the gain and the new failure belong in the release note, reported on separate rows.
Treat a model update, a prompt edit, and an index rebuild as events that reopen measurement. Version-specific benchmark numbers from an outside report do not move forward automatically, and neither do your own. Privacy sits inside this loop. Arabic conversations can include health, religion, family, and political details. Decide who may read raw prompts, what must be redacted before an example becomes a permanent test case, and how long raw logs are kept.
What do Arabic LLM benchmarks actually establish?
They establish how specific systems behaved on specific datasets. They do not certify a model for a new country, dialect, domain, or later release. No population-wide rate of Arabic agent error or cultural harm is available from the sources behind this guide. Quote a benchmark number with the paper, the task, the model version, and the date, and keep it inside that scope.
Four research efforts cover different failure families, which is why a single leaderboard is a weak proxy for a deployment decision:
- AraHalluEval (Alansari and Luqman, ArabicNLP 2025) measures hallucination with 12 fine-grained indicators on 12 LLMs, for Arabic and multilingual systems, in generative question answering and summarization. Factual hallucinations were more prevalent than faithfulness errors in that evaluated set.
- AraSafe (Findings of EMNLP 2025) measures Arabic safety and reports blind spots. The study covers four Arabic-centric chat models and three multilingual chat models.
- AraDiCE (COLING 2025) measures dialectal and cultural capabilities. Read which varieties and tasks the paper includes before you compare a subscore with your users. A variety the paper did not test remains your own measurement job.
- AraTrust (COLING 2025) measures Arabic trustworthiness, separate from a grammar or fluency result.
The Stanford Institute for Human-Centered Artificial Intelligence describes another widely cited bundle in the 2026 AI Index Report, section 3.7. HELM Arabic, as that report presents it, covers seven Arabic-language benchmarks spanning academic assessment, grammar, and region-specific safety. The report’s mean scores for three systems are below. Checked Nov 2026 against that report.
| System, as named in the report | HELM Arabic mean |
|---|---|
| Arabic.ai LLM-X | 0.86 |
| Gemini 2.5 Flash | 0.82 |
| GPT-5.1 | 0.81 |
These figures are the report’s HELM Arabic results. They are not a universal accuracy meter, and they are not evidence that any listed system is safe for a particular country, dialect, or high-impact task. The same report observes that rankings drawn from English-centric evaluations need not hold once local usage, dialect, and cultural references are represented. A shortlist taken from an English leaderboard still has to be measured again on your Arabic tasks, with your sources and your reviewers.
Use outside benchmarks to choose what to measure and to see where published systems have struggled on tasks like yours. Use your test set, your citation checks, and your community review to decide what to ship. When an internal memo cites 0.86, 0.82, or 0.81, attach the AI Index section, the system name as printed there, and the statement that the figure is a mean across the report’s HELM Arabic mix.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I compare models on the same Arabic task?
Compare candidates on the same prompts, the same sources, and the same communities. A practical comparison uses the axes below. Axes that go beyond any one paper’s official metrics are a deployment checklist, not a score that AraHalluEval, AraSafe, AraDiCE, AraTrust, HELM, or NIST publishes as a single index.
| Axis | What your evaluation has to show |
|---|---|
| Factuality and faithfulness | How often the answer is supported, and how often it changes the source, on your documents |
| Citation support | Whether each displayed citation supports the adjacent claim for the user’s region and period |
| Dialect comprehension and generation | Understanding and reply quality in each variety the spec names |
| Cultural and safety behavior | Results on sensitive prompts written in those varieties, plus ordinary prompts that should be answered |
| Human reviewer judgments | Accept, reject, and disagree rates from reviewers who know the community and the task |
| Operational fit | Latency, privacy and data-handling terms, and whether you can refresh evidence without waiting on a vendor release |
Hold the prompt, the index, and the rubric fixed while you swap models, or you will compare a bundle of changes and learn nothing about the model. Record abstention separately so a cautious model and a reckless model are visible as different patterns. Record cost only as your own procurement data; published HELM means are quality snapshots from the AI Index, not price quotes and not latency measurements.
A model that leads a general Arabic mean can still lose on your dialect slice or on citation support. The AI Index warning about localized rankings is the reason to keep the table multi-column. Ship the candidate whose worst high-severity cell, on the communities you serve, is acceptable under the harm threshold. A higher mean with a failing safety or citation cell loses to a lower mean that passes those cells.
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Ask vendors for version strings, knowledge-cutoff or index-update dates, and the retention rules for prompts. Those facts belong in the operational-fit row. A vendor benchmark slide that reports a single Arabic percentage, with no task list and no variety list, does not fill any cell in the table above.
What breaks in real use, and how do I recover?
The failures below are the patterns this measurement design is built to catch. The recovery in each row is a change to sources, tests, review, or release rules. None of these rows is a measured frequency, because the published studies do not provide a population-wide rate.
| What you see | What usually produced it | Recovery |
|---|---|---|
| A confident factual answer with no usable source | The model answered from parameters, or retrieval missed | Withhold unsupported factual claims on that topic until a span is attached and checked |
| A citation that does not support the sentence | Links were grouped at the bottom, or the reference does not say what the sentence says | Match each citation to one claim; drop the claim when the match fails |
| Fluent formal Arabic that misses the user’s meaning | Items and reviewers covered Modern Standard Arabic only | Add variety-specific items and reviewers, and report dialect as its own metric |
| Tone that one region accepts and another finds rude | One community’s politeness level was treated as universal | Split communities in the set and keep disagreement notes |
| A safety control that works in English and fails in Arabic | Rules and adversarial prompts were translated late, or only in formal Arabic | Author safety tests and constraints in the target varieties |
| Refusals on ordinary local questions | The safety set lacked benign dialect examples | Add low-risk local items and grade refusal separately from harm |
| A strong offline score and weaker live answers | New questions, stale sources, or a model change | Compare production with pre-deployment metrics by error type, then refresh sources and tests |
| A serious miss hidden inside an average | Varieties, tasks, and severities were pooled | Report by variety, task, and severity, and let the worst high-severity cell govern |
| A summary step that drops a condition | An intermediate paraphrase was trusted without a faithfulness check | Check summaries against spans before a later step uses them |
| A once-valid page that no longer supports the claim | Monitoring checked that the URL opened and stopped there | Re-read the span on the monitoring schedule and after the publisher issues a correction |
When you change one control, rerun the neighboring dimensions. Citation blocking, dialect phrasing, and refusal rules interact. The release note should show the dimension you fixed and the dimensions you rechecked, with the version string of the model and the index.
If live traffic introduces a variety you did not list in the deployment spec, pause automatic answers on high-impact topics for that traffic until reviewers who know the variety have a test slice and a scored sample. Adding the variety to a prompt in a single line, without items or reviewers, recreates the original gap.
What has to be true before a release?
Ship a version when the following are true for the communities in scope. This is the prevention system. A stronger base model can raise the ceiling on fluency and on some benchmark means. It leaves the source check, the local reviewers, and the decision to abstain in your hands.
- The deployment spec names communities, varieties, register, channel, script conventions, tasks, and the harm threshold.
- Factual topics retrieve sources, and every displayed citation has been checked against the adjacent claim, including date and place.
- Conflict, missing evidence, and out-of-scope local context produce a stated limit, a clarifying question, or a handoff.
- The test set covers dialect, code-switching, spelling and transliteration, sensitive cases, and ordinary cases, with provenance and expected behavior on each item.
- Factuality, faithfulness, citations, dialect and register, cultural appropriateness, safety, and uncertainty behavior are reported separately, by variety and task, with coverage and severity.
- Reviewers who know the target context have scored a current sample. Disagreements are stored with reasons.
- High-impact categories have a named human escalation path and a user-facing waiting state.
- Monitoring can sample live outputs, compare them with pre-deployment metrics, retire stale sources, and add new failures to the regression slice.
- The model version, index version, and prompt version are stored next to the scores.
Revisit the spec when you add a country, a variety, or a high-impact topic. The previous checklist describes the previous scope. Arabic performance is contextual: the community, the variety, the register, and the task decide which evidence counts, and a later release has to meet that evidence again.
Frequently Asked Questions
How often should I refresh the Arabic sources an agent is allowed to cite?
NIST AI 600-1 requires source and citation checks during ongoing monitoring and does not name a universal interval such as 30 or 90 days. Refresh when the authority behind the fact publishes a change, and refresh again after any model swap or index rebuild; a school calendar or eligibility rule needs that check sooner than a stable grammar note. Retire the old document in the index on the same day and write the retirement date in the source log so retrieval cannot surface it.
What metadata should I keep on every Arabic test example?
Store the variety, the region the item represents, the register, the script, a code-switching flag, the risk tier, and how the item was obtained, plus the adjudicator’s variety when a person labels it. Those fields let you filter a regression to one community after a release without rereading the full set. The ACL work on responsible annotation treats this kind of provenance as part of careful Arabic data labeling, because the labeler’s background changes how cultural items should be read.
Should an Arabic agent reply in the user’s dialect or in Modern Standard Arabic?
The benchmarks do not pick one outgoing variety for every product. Official, medical, and legal steps are often clearer in a formal register when the reader must follow conditions exactly, while local customer chat usually fits better when politeness and wording respect the user’s variety. Write the choice into the deployment spec, then score incoming understanding and outgoing variety as two results so a formal reply that missed the question cannot pass on style alone.
Who is accountable when a high-impact Arabic answer is wrong?
Accountability stays with the organization that deploys the agent, in the job named by the escalation policy for that topic. A provider’s model card or terms of service does not appoint the local approver for a medical, legal, financial, or child-safety reply. Put that job title in the release notes for the version that went live, along with the path the person used to halt or edit the answer.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Can I fine-tune on Arabic web text and treat cultural review as already done?
A scrape copies the factual mistakes, outdated pages, and stereotypes already present in those pages, and it does not record which variety a reviewer considered appropriate. Findings research on responsible Arabic annotation calls for cultural and sociolinguistic care in labeling; unreviewed web text does not supply that labeling. Fine-tuning can still be one training step, and sensitive traffic still needs the same citation checks and community review before it is shown to users.
