TigreGotico/jevhakim-4b
Text Classification • Updated • 19 • 1
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Jevhakim decisions
Content warning. The safety and hate-speech sources contain harmful requests (violence, self-harm, hate, illegal activity) and offensive messages. They are here so that a model can learn to detect them.
This dataset holds typed decisions in Arabic, in Modern Standard Arabic and the dialects, for training Jevhakim, a decision model for Arabic. A record is a state (text or JSON) and one or more questions. Each question is a choice (pick one option), a score (an ordered level) or a noul (yes or no), with its label. The format is the training format of Kev, an open model that answers this request shape with calibrated probabilities in one forward pass.
The data comes from 80 public datasets on the Hugging Face Hub. 58 sources give training data and 22 are for evaluation only. Every label comes from the source dataset: an answer key, a class annotation, a rating, a preference pair, or a wrong option taken from another row. No model wrote any label.
TigreGotico/jevhakim-0.8b and TigreGotico/jevhakim-4b are Kev trained on train.jsonl for two epochs, calibrated on calib.jsonl and measured on test.jsonl.
Files
| File | Requests | Content |
|---|---|---|
train.jsonl |
442,627 | training requests |
dev.jsonl |
50,496 | model selection |
calib.jsonl |
40,244 | temperature fitting |
test.jsonl |
119,903 | held-out evaluation, including the evaluation-only sources (141,217 questions) |
manifest.json |
per source: dataset revision, licence, variety, rows read, requests per split before the merge, and each drop reason with its count |
The training requests hold 274,958 choice, 182,372 noul and 64,571 score questions.
The state is a string in some records and a JSON object in others, as in the Jev request format. One table schema cannot hold that column, so the Hub viewer is off. Read the files as JSON lines:
import json
from huggingface_hub import hf_hub_download
path = hf_hub_download("TigreGotico/jevhakim-decisions", "train.jsonl", repo_type="dataset")
records = [json.loads(line) for line in open(path, encoding="utf-8")]
To train Kev on it, run kev.train --data train.jsonl --init_from jaredpalmer/kev-0.8b --suite evals/v7/decision-v7 --replay 12576 --p_none 0 --p_none_distract 0 --p_distract 0. The replay mixes Kev's 12,576 English decision records into the run. The three zeros turn off Kev's English "none of the above" options and English distractors.
Record format
{"state": {"message": "صاغر لي هاد الجماعات اللي يلبسو العباءة، ..."},
"questions": {"safety_class": {"type": "choice", "instructions": "صنِّف رسالة المستخدم بحسب نوع الخطر فيها، إن وُجد.",
"criteria": {"safe": "رسالة آمنة", "hate_harassment": "كراهية أو تحرّش", "...": "..."},
"label": "hate_harassment", "src": "guardrail_class"},
"unsafe": {"type": "noul", "instructions": "هل هذه الرسالة غير آمنة؟", "label": true, "src": "..."}},
"_meta": {"source": "guardrail_unseen", "repo": "oddadmix/arabic-guardrail", "revision": "...", "split": "test",
"variety": "synthetic", "id": "...", "group_id": "...", "text_sha256": "..."}}
Keys are code identifiers, so they are in English and only the values are in Arabic, as in a request a developer writes: the state's field names, the question ids and the names of choice options. Some sources (jev_darija, jev_ar_bench, the intent question of massive_ar) have option names with no Arabic description, such as billing_payment.
The label is the option key for choice, true or false for noul, and the level index from 0 for score. Option order is shuffled per record, so the key never carries the answer. _meta.revision is the commit of the source dataset that the build read.
Splits
A record goes to train, dev, calib or test by a hash of its group key, weighted 80/10/5/5. Records that must stay together share a group key, for example reposts of one tweet that differ only in emoji or punctuation. Sources with their own splits keep them. A training record is removed when its state appears in any held-out split of any source. This removed 9,800 training requests, mostly tweets that several LlamaLens configurations share.
Each source gives at most 10,000 training requests and 1,000 requests to each held-out split. Evaluation-only sources give up to 5,000 test requests and nothing to train. guardrail_unseen and prompt_routing_unseen hold unseen rows of two task kinds that the training data has, so they test new rows, not new tasks.
Sources
The variety column is msa for Modern Standard Arabic, egy for Egyptian, lev for Levantine, mixed for sources that span several varieties, mt-ar for machine translations into Arabic and synthetic for text generated by language models. The counts are those of the published files. The licence column gives the licence and where it is stated: the dataset card, or the original dataset upstream.
| Source | Dataset | Variety | Train | Dev | Calib | Test | Licence |
|---|---|---|---|---|---|---|---|
| aoc_aldi | arbml/AOC_ALDi |
mixed | 9,996 | 1,000 | 1,000 | 1,000 | not stated (AOC upstream: research) |
| arabic_nli | Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class |
mt-ar | 10,000 | 1,000 | 1,000 | 1,000 | Apache-2.0 (Hub card) |
| arafacts | QCRI/LlamaLens-Arabic |
mixed | 4,098 | 622 | 252 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| arcyc_cyberbullying | QCRI/LlamaLens-Arabic |
mixed | 2,082 | 443 | 186 | 887 | CC-BY-NC-SA-4.0 (Hub card) |
| arcyc_offensive | QCRI/LlamaLens-Arabic |
mixed | 2,082 | 446 | 173 | 891 | CC-BY-NC-SA-4.0 (Hub card) |
| arpro_propaganda | QCRI/LlamaLens-Arabic |
msa | 5,517 | 672 | 343 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| arsarcasm | QCRI/LlamaLens-Arabic |
mixed | 8,207 | 1,000 | 542 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| arsas | QCRI/LlamaLens-Arabic |
mixed | 10,000 | 1,000 | 817 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| asnd | QCRI/LlamaLens-Arabic |
msa | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| barec | CAMeL-Lab/BAREC-Corpus-v1.0 |
msa | 9,981 | 1,000 | 1,000 | 0 | CC-BY-SA-4.0 (Hub card) |
| brad | arbml/BRAD |
mixed | 9,994 | 1,000 | 1,000 | 1,000 | not stated (BRAD upstream: research) |
| commonsense_validation | arbml/Commonsense_Validation |
mt-ar | 9,386 | 650 | 604 | 348 | not stated (SemEval-2020 task 4 upstream: CC-BY-SA-4.0) |
| covid_factuality | QCRI/LlamaLens-Arabic |
mixed | 1,704 | 315 | 230 | 917 | CC-BY-NC-SA-4.0 (Hub card) |
| ct22_attention | QCRI/LlamaLens-Arabic |
mixed | 1,684 | 1,000 | 158 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| ct22_claim | QCRI/LlamaLens-Arabic |
mixed | 1,704 | 315 | 230 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| ct22_harmful | QCRI/LlamaLens-Arabic |
mixed | 1,681 | 1,000 | 169 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| ct24_checkworthy | QCRI/LlamaLens-Arabic |
mixed | 5,025 | 1,000 | 470 | 610 | CC-BY-NC-SA-4.0 (Hub card) |
| dpo_norobots | 2A2I/NoRobots-AceGPT.13B.Chat-DPO |
mt-ar | 7,271 | 932 | 417 | 450 | Apache-2.0 (Hub card) |
| egy_translation | oddadmix/egyptian-arabic-english-translation-50k |
egy | 10,000 | 1,000 | 1,000 | 1,000 | Apache-2.0 (Hub card) |
| emotional_tone | QCRI/LlamaLens-Arabic |
egy | 6,609 | 995 | 412 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| guardrail | oddadmix/arabic-guardrail |
synthetic | 10,000 | 1,000 | 1,000 | 1,000 | Apache-2.0 (Hub card) |
| habibi | arbml/Habibi |
mixed | 10,000 | 1,000 | 1,000 | 1,000 | not stated (lyrics; research use only) |
| hard | Elnagara/hard |
mixed | 10,000 | 1,000 | 1,000 | 1,000 | not stated (card: unknown) |
| hate_superset | manueltonneau/arabic-hate-speech-superset |
mixed | 10,000 | 1,000 | 860 | 964 | not stated on the card; gated: no use that harms people; upstream licences vary |
| hatetweets_sentiment | QCRI/LlamaLens-Arabic |
mixed | 9,999 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| jev_darija | mballouch/jev-darija-telecom |
synthetic | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-4.0 (Hub card) |
| labr | mohamedadaly/labr |
mixed | 9,246 | 1,000 | 601 | 1,000 | not stated (card: unknown) |
| llamalens_stance | QCRI/LlamaLens-Arabic |
msa | 2,479 | 755 | 173 | 379 | CC-BY-NC-SA-4.0 (Hub card) |
| madar | macabdul9/NADI-Sample02 |
mixed | 10,000 | 1,000 | 1,000 | 1,000 | not stated; MADAR upstream: non-commercial research licence |
| massive_ar | SetFit/amazon_massive_intent_ar-SA |
msa | 10,000 | 1,000 | 629 | 1,000 | CC-BY-4.0 (upstream AmazonScience/massive) |
| mqa_faq | clips/mqa |
msa | 10,000 | 1,000 | 1,000 | 1,000 | CC0-1.0 (Hub card) |
| nadi | ashabrawy/NADI-Balanced-Country-DID |
mixed | 7,962 | 0 | 504 | 1,000 | not stated; NADI shared-task data, research use for registered teams |
| nawah | oddadmix/nawah-dialect-data |
mixed | 10,000 | 1,000 | 1,000 | 1,000 | Apache-2.0 (Hub card) |
| nemotron_safety | nvidia/Nemotron-Safety-Guard-Dataset-v3 |
mt-ar | 9,996 | 1,000 | 1,000 | 1,000 | CC-BY-4.0 (Hub card) |
| news_credibility | QCRI/LlamaLens-Arabic |
msa | 8,131 | 1,000 | 540 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| news_headline | QCRI/LlamaLens-Arabic |
msa | 890 | 100 | 49 | 153 | CC-BY-NC-SA-4.0 (Hub card) |
| nsurl_q2q | arbml/nsurl_2019_task8_train |
msa | 9,471 | 1,000 | 609 | 596 | not stated (NSURL-2019 task 8 upstream: research) |
| osact4_hate | QCRI/LlamaLens-Arabic |
mixed | 3,819 | 1,000 | 311 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| osact4_offensive | QCRI/LlamaLens-Arabic |
mixed | 3,846 | 1,000 | 303 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| palmx_culture | UBC-NLP/palmx_2025_subtask1_culture |
msa | 1,870 | 498 | 129 | 1,000 | PalmX Data License: non-commercial research use (Hub card) |
| palmx_islamic | UBC-NLP/palmx_2025_subtask2_islamic |
msa | 553 | 286 | 38 | 983 | PalmX Data License: non-commercial research use (Hub card) |
| polyguardmix | ToxicityPrompts/PolyGuardMix |
mt-ar | 9,998 | 1,000 | 1,000 | 1,000 | CC-BY-4.0 (Hub card) |
| prompt_routing | oddadmix/arabic-prompt-routing |
synthetic | 10,000 | 1,000 | 1,000 | 1,000 | Apache-2.0 (Hub card) |
| qadi | Abdelrahman-Rezk/Arabic_Dialect_Identification |
mixed | 10,000 | 1,000 | 1,000 | 1,000 | not stated on the card; upstream QADI Apache-2.0 |
| rag_chat | oddadmix/arabic-rag-chat-30K |
synthetic | 10,000 | 1,000 | 1,000 | 850 | Apache-2.0 (Hub card) |
| rag_support | oddadmix/arabic-rag-support-25K |
synthetic | 10,000 | 1,000 | 1,000 | 889 | Apache-2.0 (Hub card) |
| reviews100k | QCRI/LlamaLens-Arabic |
mixed | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| rule_checking | oddadmix/arabic-rule-checking |
synthetic | 10,000 | 1,000 | 1,000 | 1,000 | Apache-2.0 (Hub card) |
| sanad_akhbarona | QCRI/LlamaLens-Arabic |
msa | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| sanad_alarabiya | QCRI/LlamaLens-Arabic |
msa | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| sanad_alkhaleej | QCRI/LlamaLens-Arabic |
msa | 9,896 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| shami | arbml/Shami |
lev | 10,000 | 1,000 | 1,000 | 1,000 | not stated (Shami corpus upstream: research) |
| stsb_ar | Omartificial-Intelligence-Space/Arabic-stsb |
mt-ar | 5,354 | 1,000 | 349 | 1,000 | Apache-2.0 (Hub card) |
| thatiar_subjective | QCRI/LlamaLens-Arabic |
msa | 2,227 | 467 | 146 | 748 | CC-BY-NC-SA-4.0 (Hub card) |
| tydiqa_answerable | copenlu/answerable_tydiqa |
msa | 10,000 | 1,000 | 1,000 | 605 | Apache-2.0 (Hub card) |
| ultimate_news | QCRI/LlamaLens-Arabic |
msa | 9,869 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card) |
| xlsum_faithful | QCRI/LlamaLens-Arabic |
msa | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-NC-SA-4.0 (Hub card; XL-Sum upstream CC-BY-NC-SA-4.0) |
| xnli_ar | facebook/xnli |
mt-ar | 10,000 | 1,000 | 1,000 | 1,000 | CC-BY-NC-4.0 (XNLI upstream README) |
| abb | silma-ai/arabic-broad-benchmark |
mixed | 0 | 0 | 0 | 125 | Apache-2.0 (Hub card) |
| alghafa_native | OALL/AlGhafa-Arabic-LLM-Benchmark-Native |
mixed | 0 | 0 | 0 | 5,000 | not stated |
| arabculture | MBZUAI/ArabCulture |
mixed | 0 | 0 | 0 | 3,438 | CC-BY-NC-SA-4.0 (Hub card) |
| arabic_exams | inceptlabs/Arabic_EXAMS-Redux |
msa | 0 | 0 | 0 | 530 | CC-BY-SA-4.0 (Hub card) |
| arabic_mmlu | MBZUAI/ArabicMMLU |
msa | 0 | 0 | 0 | 5,000 | CC-BY-NC-4.0 (Hub card) |
| arapro | humain-ai/AraPro |
msa | 0 | 0 | 0 | 4,894 | not stated |
| aratrust | asas-ai/AraTrust |
msa | 0 | 0 | 0 | 519 | MIT (Hub card) |
| aratruthfulqa | humain-ai/AraTruthfulQA |
msa | 0 | 0 | 0 | 536 | not stated |
| barec_test | CAMeL-Lab/BAREC-Corpus-v1.0 |
msa | 0 | 0 | 0 | 5,000 | CC-BY-SA-4.0 (Hub card) |
| belebele_ar | facebook/belebele |
mixed | 0 | 0 | 0 | 5,000 | CC-BY-SA-4.0 (Hub card) |
| etec | humain-ai/Etec |
msa | 0 | 0 | 0 | 1,859 | not stated |
| global_piqa_ar | mrlbenchmarks/global-piqa-parallel |
mixed | 0 | 0 | 0 | 1,322 | CC-BY-SA-4.0 (Hub card) |
| guardrail_unseen | oddadmix/arabic-guardrail |
synthetic | 0 | 0 | 0 | 5,000 | Apache-2.0 (Hub card) |
| hatecheck_ar | Paul/hatecheck-arabic |
mixed | 0 | 0 | 0 | 3,530 | CC-BY-4.0 (Hub card) |
| ien_mcq | humain-ai/IEN_MCQ |
msa | 0 | 0 | 0 | 5,000 | not stated |
| ien_tf | humain-ai/IEN_TF |
msa | 0 | 0 | 0 | 5,000 | not stated |
| include_ar | CohereLabs/include-base-44 |
msa | 0 | 0 | 0 | 543 | Apache-2.0 (Hub card) |
| jev_ar_bench | atmaneayoub/jev-ar-bench |
synthetic | 0 | 0 | 0 | 4,974 | CC-BY-4.0 (Hub card) |
| madinahqa | MBZUAI/MadinahQA |
msa | 0 | 0 | 0 | 943 | CC-BY-NC-4.0 (Hub card) |
| polyguard_ar | ToxicityPrompts/PolyGuardPrompts |
mt-ar | 0 | 0 | 0 | 1,725 | CC-BY-4.0 (Hub card) |
| prompt_routing_unseen | oddadmix/arabic-prompt-routing |
synthetic | 0 | 0 | 0 | 5,000 | Apache-2.0 (Hub card) |
| system_one_ar | najdresearch/system-one |
mixed | 0 | 0 | 0 | 2,695 | per pack: CC-BY-4.0, CC-BY-SA-4.0, Apache-2.0, MIT (Hub card) |
| Total | 442,627 | 50,496 | 40,244 | 119,903 |
Licence
This dataset is released under CC BY-NC-SA 4.0, for non-commercial research. Many sources are non-commercial, and the QCRI LlamaLens sets, BAREC, belebele and others are share-alike.
The cards of 18 sources state no licence: alghafa_native, aoc_aldi, arapro, aratruthfulqa, brad, commonsense_validation, etec, habibi, hard, hate_superset, ien_mcq, ien_tf, labr, madar, nadi, nsurl_q2q, qadi, shami. Several of them have research-only terms upstream: NADI is shared-task data for registered teams, MADAR has a non-commercial research licence, the Habibi lyrics are for research, and the PalmX sets are under the PalmX Data License for non-commercial research. The hate superset card has no licence, its gate forbids a use that harms people, and its upstream sets have their own terms. Check each source's terms before any use beyond research. If you hold rights to one of these sources and want it removed, open a discussion on this repository.
Limits
- Part of the data is machine-translated or generated. Of the 442,627 training requests, 62,005 come from sources translated from English (arabic_nli, commonsense_validation, dpo_norobots, nemotron_safety, polyguardmix, stsb_ar, xnli_ar) and 60,000 from sources generated by language models (guardrail, jev_darija, prompt_routing, rag_chat, rag_support, rule_checking).
- Dialect coverage is uneven. By source, 130,884 training requests are MSA, 163,129 come from sources that mix varieties, 16,609 are Egyptian and 10,000 are Levantine.
- Dialect labels are by country or region of the writer. In qadi and habibi, the converter drops a text when marker words of another regional group outnumber those of the labelled one. That removes only the clear mistakes.
- Several tweet sets have noisy labels. In covid_factuality, the ct22 sets, osact4 and hate_superset, some labels are open to argument. arpro_propaganda labels a paragraph true when it has any propaganda technique. These are the upstream labels.
- hard has no three-star level. The HARD dataset has no three-star reviews. The score keeps five levels, and the middle level has no examples.
- The wrong options in faithfulness and retrieval sources come from other rows. They are easier than real errors.
- alghafa_native has upstream damage. Some rows hold broken PDF text with stray diacritics.
Attribution
This dataset is a derived work. The text and the labels belong to the authors of the 52 Hub datasets below and of the original corpora they were built from. The build only rewrote each row as a typed question, wrote the instructions in Arabic and shuffled the options. If you use a source, cite its authors: the BibTeX each card gives is under the table, and the original work column names the corpus to cite when the Hub copy is a re-upload.
| Hub dataset | Sources here | Published by | Original work |
|---|---|---|---|
2A2I/NoRobots-AceGPT.13B.Chat-DPO |
dpo_norobots | 2A2I | No Robots (Hugging Face H4), translated, with AceGPT-13B-chat answers as the rejected side |
Abdelrahman-Rezk/Arabic_Dialect_Identification |
qadi | Abdelrahman-Rezk | QADI, QCRI Arabic Dialect Identification tweets (Abdelali et al., 2021) |
arbml/AOC_ALDi |
aoc_aldi | arbml | Arabic Online Commentary dataset (Zaidan and Callison-Burch, 2011) with ALDi dialectness levels (Keleg et al., 2023) |
arbml/BRAD |
brad | arbml | BRAD, Book Reviews in Arabic Dataset (Elnagar and Einea, 2016) |
arbml/Commonsense_Validation |
commonsense_validation | arbml | Arabic translation of the SemEval-2020 Task 4 commonsense validation data (Tawalbeh and Al-Smadi, 2020) |
arbml/Habibi |
habibi | arbml | Habibi, multi-dialect Arabic song lyrics corpus (El-Haj, 2020) |
arbml/nsurl_2019_task8_train |
nsurl_q2q | arbml | NSURL-2019 Task 8, semantic question similarity in Arabic (Mawdoo3) |
arbml/Shami |
shami | arbml | Shami corpus of Levantine dialects (Abu Kwaik et al., 2018) |
asas-ai/AraTrust |
aratrust | asas-ai | |
ashabrawy/NADI-Balanced-Country-DID |
nadi | ashabrawy | NADI shared tasks on Nuanced Arabic Dialect Identification (Abdul-Mageed et al.) |
atmaneayoub/jev-ar-bench |
jev_ar_bench | atmaneayoub | |
CAMeL-Lab/BAREC-Corpus-v1.0 |
barec, barec_test | CAMeL-Lab | |
clips/mqa |
mqa_faq | clips | |
CohereLabs/include-base-44 |
include_ar | CohereLabs | |
copenlu/answerable_tydiqa |
tydiqa_answerable | copenlu | |
Elnagara/hard |
hard | Elnagara | HARD, Hotel Arabic Reviews Dataset (Elnagar, Khalifa and Einea, 2018) |
facebook/belebele |
belebele_ar | ||
facebook/xnli |
xnli_ar | ||
humain-ai/AraPro |
arapro | humain-ai | |
humain-ai/AraTruthfulQA |
aratruthfulqa | humain-ai | |
humain-ai/Etec |
etec | humain-ai | |
humain-ai/IEN_MCQ |
ien_mcq | humain-ai | |
humain-ai/IEN_TF |
ien_tf | humain-ai | |
inceptlabs/Arabic_EXAMS-Redux |
arabic_exams | inceptlabs | |
macabdul9/NADI-Sample02 |
madar | macabdul9 | MADAR corpus of city-level dialects (Bouamor et al., 2018) |
manueltonneau/arabic-hate-speech-superset |
hate_superset | manueltonneau | merges Let-Mi, MLMA, L-HSAB, T-HSAB, OSACT5 and other Arabic hate-speech sets; its card names each one |
mballouch/jev-darija-telecom |
jev_darija | mballouch | |
MBZUAI/ArabCulture |
arabculture | MBZUAI | |
MBZUAI/ArabicMMLU |
arabic_mmlu | MBZUAI | |
MBZUAI/MadinahQA |
madinahqa | MBZUAI | |
mohamedadaly/labr |
labr | mohamedadaly | LABR, Large-scale Arabic Book Reviews (Aly and Atiya, 2013) |
mrlbenchmarks/global-piqa-parallel |
global_piqa_ar | mrlbenchmarks | |
najdresearch/system-one |
system_one_ar | najdresearch | |
nvidia/Nemotron-Safety-Guard-Dataset-v3 |
nemotron_safety | nvidia | |
OALL/AlGhafa-Arabic-LLM-Benchmark-Native |
alghafa_native | OALL | |
oddadmix/arabic-guardrail |
guardrail, guardrail_unseen | oddadmix | |
oddadmix/arabic-prompt-routing |
prompt_routing, prompt_routing_unseen | oddadmix | |
oddadmix/arabic-rag-chat-30K |
rag_chat | oddadmix | |
oddadmix/arabic-rag-support-25K |
rag_support | oddadmix | |
oddadmix/arabic-rule-checking |
rule_checking | oddadmix | |
oddadmix/egyptian-arabic-english-translation-50k |
egy_translation | oddadmix | |
oddadmix/nawah-dialect-data |
nawah | oddadmix | |
Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class |
arabic_nli | Omartificial-Intelligence-Space | |
Omartificial-Intelligence-Space/Arabic-stsb |
stsb_ar | Omartificial-Intelligence-Space | |
Paul/hatecheck-arabic |
hatecheck_ar | Paul | Multilingual HateCheck (Röttger et al., 2022) |
QCRI/LlamaLens-Arabic |
arafacts, arcyc_cyberbullying, arcyc_offensive, arpro_propaganda, arsarcasm, arsas, asnd, covid_factuality, ct22_attention, ct22_claim, ct22_harmful, ct24_checkworthy, emotional_tone, hatetweets_sentiment, llamalens_stance, news_credibility, news_headline, osact4_hate, osact4_offensive, reviews100k, sanad_akhbarona, sanad_alarabiya, sanad_alkhaleej, thatiar_subjective, ultimate_news, xlsum_faithful | QCRI | LlamaLens (Kmainasi et al., 2024) collects many Arabic sets: ArSarcasm, ArSAS, ASND, ArCyc, ArPro, OSACT4, the CheckThat! 2022 and 2024 tasks, SANAD, XL-Sum and others; its card names the original dataset of each configuration |
SetFit/amazon_massive_intent_ar-SA |
massive_ar | SetFit | MASSIVE (FitzGerald et al., 2022), Amazon Science |
silma-ai/arabic-broad-benchmark |
abb | silma-ai | |
ToxicityPrompts/PolyGuardMix |
polyguardmix | ToxicityPrompts | |
ToxicityPrompts/PolyGuardPrompts |
polyguard_ar | ToxicityPrompts | |
UBC-NLP/palmx_2025_subtask1_culture |
palmx_culture | UBC-NLP | |
UBC-NLP/palmx_2025_subtask2_islamic |
palmx_islamic | UBC-NLP |
BibTeX from the source cards (27 entries)
@inproceedings{keleg-etal-2023-aldi,
title = "{ALD}i: Quantifying the {A}rabic Level of Dialectness of Text",
author = "Keleg, Amr and
Goldwater, Sharon and
Magdy, Walid",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.655",
doi = "10.18653/v1/2023.emnlp-main.655",
pages = "10597--10611",
abstract = "Transcribed speech and user-generated text in Arabic typically contain a mixture of Modern Standard Arabic (MSA), the standardized language taught in schools, and Dialectal Arabic (DA), used in daily communications. To handle this variation, previous work in Arabic NLP has focused on Dialect Identification (DI) on the sentence or the token level. However, DI treats the task as binary, whereas we argue that Arabic speakers perceive a spectrum of dialectness, which we operationalize at the sentence level as the Arabic Level of Dialectness (ALDi), a continuous linguistic variable. We introduce the AOC-ALDi dataset (derived from the AOC dataset), containing 127,835 sentences (17{\%} from news articles and 83{\%} from user comments on those articles) which are manually labeled with their level of dialectness. We provide a detailed analysis of AOC-ALDi and show that a model trained on it can effectively identify levels of dialectness on a range of other corpora (including dialects and genres not included in AOC-ALDi), providing a more nuanced picture than traditional DI systems. Through case studies, we illustrate how ALDi can reveal Arabic speakers{'} stylistic choices in different situations, a useful property for sociolinguistic analyses.",
}
@article{alghamdi2024aratrust,
title={AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic},
author={Alghamdi, Emad A and Masoud, Reem I and Alnuhait, Deema and Alomairi, Afnan Y and Ashraf, Ahmed and Zaytoon, Mohamed},
journal={arXiv preprint arXiv:2403.09017},
year={2024}
}
@misc{jevarbench2026,
title = {Jev-AR Bench: Arabic Intent-Routing Benchmarks},
author = {atmaneayoub},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/atmaneayoub/jev-ar-bench}}
}
@inproceedings{elmadani-etal-2025-readability,
title = "A Large and Balanced Corpus for Fine-grained {A}rabic Readability Assessment",
author = "Elmadani, Khalid N. and
Habash, Nizar and
Taha-Thomure, Hanada",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.842/"
}
@inproceedings{habash-etal-2025-guidelines,
title = "Guidelines for Fine-grained Sentence-level {A}rabic Readability Annotation",
author = "Habash, Nizar and
Taha-Thomure, Hanada and
Elmadani, Khalid N. and
Zeino, Zeina and
Abushmaes, Abdallah",
booktitle = "Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.law-1.30/"
}
@inproceedings{de-bruyn-etal-2021-mfaq,
title = "{MFAQ}: a Multilingual {FAQ} Dataset",
author = "De Bruyn, Maxime and
Lotfi, Ehsan and
Buhmann, Jeska and
Daelemans, Walter",
booktitle = "Proceedings of the 3rd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrqa-1.1",
pages = "1--13",
}
@article{romanou2024include,
title={INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge},
author={Romanou, Angelika and Foroutan, Negar and Sotnikova, Anna and Chen, Zeming and Nelaturu, Sree Harsha and Singh, Shivalika and Maheshwary, Rishabh and Altomare, Micol and Haggag, Mohamed A and Amayuelas, Alfonso and others},
journal={arXiv preprint arXiv:2411.19799},
year={2024}
}
@article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}
@inproceedings{bandarkar-etal-2024-belebele,
title = "The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants",
author = "Bandarkar, Lucas and
Liang, Davis and
Muller, Benjamin and
Artetxe, Mikel and
Shukla, Satya Narayan and
Husa, Donald and
Goyal, Naman and
Krishnan, Abhinandan and
Zettlemoyer, Luke and
Khabsa, Madian",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.44",
pages = "749--775",
}
@InProceedings{conneau2018xnli,
author = {Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin},
title = {XNLI: Evaluating Cross-lingual Sentence Representations},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing},
year = {2018},
publisher = {Association for Computational Linguistics},
location = {Brussels, Belgium},
}
@misc{arabic_exams_redux_2026,
title = {Arabic\_EXAMS-Redux: A Corrected Re-Annotation of the Arabic Subset of the EXAMS Multilingual High-School Benchmark},
author = {Abdulhamid, Ahmed and IL Labs},
year = {2026},
url = {https://huggingface.co/datasets/inceptlabs/Arabic_EXAMS-Redux}
}
@misc{oall_arabic_exams,
title = {Open Arabic Leaderboard - Arabic EXAMS},
url = {https://huggingface.co/datasets/OALL/Arabic_EXAMS}
}
@inproceedings{hardalov-etal-2020-exams,
title = {{EXAMS}: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering},
author = {Hardalov, Momchil and Mihaylov, Todor and Zlatkova, Dimitrina and Dinkov, Yoan and Koychev, Ivan and Nakov, Preslav},
booktitle = {EMNLP 2020},
year = {2020}
}
@inproceedings{tonneau-etal-2024-languages,
title = "From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets",
author = {Tonneau, Manuel and
Liu, Diyi and
Fraiberger, Samuel and
Schroeder, Ralph and
Hale, Scott and
R{\"o}ttger, Paul},
editor = {Chung, Yi-Ling and
Talat, Zeerak and
Nozza, Debora and
Plaza-del-Arco, Flor Miriam and
R{\"o}ttger, Paul and
Mostafazadeh Davani, Aida and
Calabrese, Agostina},
booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.woah-1.23",
pages = "283--311",
abstract = "Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to different cultures. In this work, we evaluate cultural bias in HS datasets by leveraging two interrelated cultural proxies: language and geography. We conduct a systematic survey of HS datasets in eight languages and confirm past findings on their English-language bias, but also show that this bias has been steadily decreasing in the past few years. For three geographically-widespread languages{---}English, Arabic and Spanish{---}we then leverage geographical metadata from tweets to approximate geo-cultural contexts by pairing language and country information. We find that HS datasets for these languages exhibit a strong geo-cultural bias, largely overrepresenting a handful of countries (e.g., US and UK for English) relative to their prominence in both the broader social media population and the general population speaking these languages. Based on these findings, we formulate recommendations for the creation of future HS datasets.",
}
@misc{ballouch2026jevdarijatelecom,
author = {Ballouch, Mohamed},
title = {Jev-Darija Telecom: Moroccan Darija Customer-Service Messages},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/mballouch/jev-darija-telecom}}
}
@misc{sadallah2025arabculture,
title={Commonsense Reasoning in Arab Culture},
author={Abdelrahman Sadallah and Junior Cedric Tonga and Khalid Almubarak and Saeed Almheiri and Farah Atif and Cahtrine Qwaider and Karima Kadaoui and Sara Shatnawi and Yaser Alesh and Fajri Koto},
year={2025},
eprint={2502.12788},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{koto2024arabicmmlu,
title={ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic},
author={"Fajri Koto and Haonan Li and Sara Shatanawi and Jad Doughman and Abdelrahman Boda Sadallah and Aisha Alraeesi and Khalid Almubarak and Zaid Alyafeai and Neha Sengupta and Shady Shehata and Nizar Habash and Preslav Nakov and Timothy Baldwin"},
booktitle={Findings of the Association for Computational Linguistics: ACL 2024},
year={2024}
}
@inproceedings{aly2013labr,
title={Labr: A large scale arabic book reviews dataset},
author={Aly, Mohamed and Atiya, Amir},
booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={494--498},
year={2013}
}
@article{mrl-workshop-2025-global-piqa,
title={Global {PIQA}: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures},
author={Tyler A. Chang and Catherine Arnett and Abdelrahman Sadallah and Abdelrahman Eldesokey and Abeer Kashar and Abolade Daud and Abosede Grace Olanihun and Adamu Labaran Mohammed and Adeyemi Praise and Adhikarimayum Meerajita Sharma and Aditi Gupta and Adril Putra Merin and Adwoa Bremang and Afitab Iyigun and Afonso Simplício and Ahmed Essouaied and Aicha Chorana and Akhil Eppa and Akintunde Oladipo and Akriti Kuri and Akshay Ramesh and Aleksei Dorkin and Alfred Malengo Kondoro and Alham Fikri Aji and Ali Eren Çetintaş and Allan Hanbury and Alou Dembele and Alp Niksarli and Álvaro Arroyo and Amin Bajand and Amol Khanna and Ana Chkhaidze and Ana Carolina Condez and Anamaria-Roberta Hartl and Andiswa Mkhonto and Andrew Hoblitzell and Andrew Tran and Angelos Poulis and Anirban Majumder and Anjali Chaudhary and Anna Vacalopoulou and Annette Kuuipolani Kanahele Wong and Annika Simonsen and Anton Kovalev and Anupam Nayak and Ashvanth S and Ayodeji Lana and Ayu Purwarianti and Bashar Alhafni and Benedict Busole and Bernard Ghanem and Bharti Nathani and Biljana Stojanovska Đurić and Blessing Ogundipe and Bolaotan Agbonile and Bragi Bergsson and Bruce Torres Fischer and Burak Tutar and Burcu Çınar and Cade Kane and Can Udomcharoenchaikit and Chadi Helwe and Chaithra Reddy Nerella and Chen Cecilia Liu and Chiamaka Nwokolo and Christopher Homan and Clément Sampebgo and Cristina España-Bonet and Cynthia Amol and Daeyoep Lee and Dan Saattrup Smart and Dana Arad and Daniil Dzenhaliou and Dasol Choi and David Liu and David Semedo and David Anugraha and Deborah Popoola and Deividas Mataciunas and Delphine Nyaboke and Dennis Owusu and Dhyuthy Krishna Kumar and Diogo Tavares and Diogo Glória-Silva and Divyanshu Goyal and DongGeon Lee and E. Kelly Buchanan and Ebele Nwamaka Anajemba and Egonu Ngozi Grace and Elena Mickel and Elias Herranen and Eliza Acharya and Eman Nisar and Emile Anand and Emmanuel Habumuremyi and Emuobonuvie Maria Ajiboye and Eryawan Presma Yulianrifat and Esther Adenuga and Ewa Rudnicka and Faith Itiola and Faran Taimoor Butt and Fareeha Fayyaz Sheikh and Fathima Thekkekara and Fatima Haouari and Faustin Nsengiyumva and Fenal Ashokbhai Ilasariya and Filbert Aurelian Tjiaranata and Firas Laakom and Francesca Grasso and Francesco Periti and Francesco Orabona and Gbenga Kayode Solomon and Genta Indra Winata and Gia Nghia Ngo and Gloria Udhedhe-oze and Gonçalo Vinagre and Gopi Naga Sai Ram Challagolla and Gorka Urbizu-Garmendia and Gouthami Vadithya and Guijin Son and Gulnaz Abdykadyrova and Gyan Swaroop Mohapatra and Hafeez Ullah and Hafsteinn Einarsson and Hai Hu and Hamidreza Saffari and Hamza Zaidi and Haopeng Zhang and Harethah Abu Shairah and Harry Vuong and Hele-Andra Kuulmets and Hitesh Laxmichand Patel and Houda Bouamor and Hwanjo Yu and Iben Nyholm Debess and İbrahim Ethem Deveci and Ikhlasul Akmal Hanif and Ikhyun Cho and Inês Vieira and Inês Calvo and Isaac Manzi and Ismael Illa Salifou and Ismail Daud and Ismail Yusuf and Itay Itzhak and Ivan Zhelyazkov and Ivan Belashkin and Ivan Spada and Jacob Brinton and Jafar Isbarov and Jaka Čibej and Jan Kocoń and Jan Cuhel and Jauza Krito and Jebish Purbey and Jennifer Za and Jennifer Mickel and Jenny Kunz and Jessica Ratovondranto and Jeyarajalingam Varsha and Jihae Jeong and Jimena Tena Dávalos and Jinu Lee and João Magalhães and John Seon Keun Yi and Jongin Kim and Joseph Chataignon and Joseph Marvin Imperial and Jubeerathan Thevakumar and Judith Land and Julia Alekseenko and Junchen Jiang and Jungwhan Kim and Kairit Sirts and Kamesh R and Kamesh V and Kanda Tshinu and Kätriin Kukk and Kaustubh Ponkshe and Kavsar Huseynova and Ke He and Kenneth Enevoldsen and Kent Joshua Alvarez and Kerem Zaman and Khalil Mrini and Kian Kyars and Komal Gour and Krishnakumar Lainitha and Krister Kruusmaa and Kunal Mukherjee and Kusum Chouhan and Laura Castro and Laura M. Porrino-Moscoso and Lenny Sivi Za Nzambi and Leshem Choshen and Levent Sencan and Lilja Øvrelid and Lisa Alazraki and Loretta Oma Jones and Lovina Ehimen-Ugbede and Luheerathan Thevakumar and Luxshan Thavarasa and Mahnoor Malik and Mamadou K. Keita and Mansi Jangid and Marco De Santis and Marcos Garcia and Marek Šuppa and Mariam D'Ciofalo and Marii Ojastu and Marium Attaullah and Maryam Sikander and Mausami Narayan and Maximos Skandalis and Mehak Mehak and Mehmet İlteriş Bozkurt and Melaku Bayu and Menan Velayuthan and Mhasilenuo Vizo and Michael Leventhal and Michał Marcińczuk and Mina Almasi and Mirna Potočnjak and Mithil Bangera and Mohammadamin Shafiei and Mohiba Ansari and Mridul Sharma and Mrityunjaya Indoria and Mughees Ur Rehman and Muhammad Ravi Shulthan Habibi and Murat Kolić and Murat Barkın Kınay and Nada Galant and Naina Singh Rathore and Naphat Permpredanun and Narada Maugin and Nathalie Norman and Nicholas Kluge Corrêa and Nikola Ljubešić and Nirmal Thomas and Nisansa de Silva and Nisheeth Joshi and Nitish Ponkshe and Nizar Habash and Nneoma Udeze and Noel Thomas and Noémi Ligeti-Nagy and Nouhoum Coulibaly and Odunayo Ogundepo and Odunayo Kareemat Buliaminu and Oghojafor Godswill Fejiro and Okechukwu God'spraise and Olanrewaju Samuel and Olaoye Deborah Oluwaseun and Olasoji Akindejoye and Olga Snissarenko and Onyinye Anulika Chiemezie and Orkun Kınay and Osman Tursun and Oyelade Oluwafemi Joshua and Oyesanmi Fiyinfoluwa and Pablo Rodríguez and Pablo Gamallo and Palak Arora and Pedro Valente and Peter Rupnik and Philip Oghenesuowho Ekiugbo and Prakhar Agarwal and Pramit Sahoo and Prokopis Prokopidis and Pua Niau-Puhipau and Quadri Yahya and Rachele Mignone and Raghav Singhal and Rahul Raja and Ram Mohan Rao Kadiyala and Raphael Merx and Rasmus Larsen and Ratnavel Rajalakshmi and Rishav Ghosh and Romina Oji and Ron Kekeha Solis and Rui Guerra and Rushikesh Zawar and Sa'ad Nasir Bashir and Saeed Alzaabi and Sahil Sandeep and Sai Pavan Batchu and Sai Sandeep Kantareddy and Saleha Muzammil and Salsabila Zahirah Pranida and Sam Buchanan and Samuel Rutunda and Sander Land and Sarah Sulollari and Sardar Ali and Saroj Sapkota and Sarveswaran Kengatharaiyer and Saulius Tautvaisas and Sayambhu Sen and Sayantani Banerjee and Sebastien Diarra and Segun Afolayan and Senthilnathan M and Sewoong Lee and Shaan Shah and Shankar Venkitachalam and Sharifa Djurabaeva and Sharon Ibejih and Shivanya Shomir Dutta and Siddhant Gupta and Silvia Paniagua Suárez and Sina Ahmadi and Sivasuthan Sukumar and Siyuan Song and Snegha A and Sokratis Sofianopoulos and Sona Elza Simon and Sonja Benčina and Sophie Gvasalia and Sphurti More and Spyros Dragazis and Stefan Milosavljević and Stephan P. Kaufhold and Suba S and Sultan Alrashed and Surangika Ranathunga and Taiga Someya and Taja Kuzman Pungeršek and Tal Haklay and Tasi'u Jibril and Tatsuya Aoyama and Tea Abashidze and Terenz Jomar Dela Cruz and Terra Blevins and Themistoklis Nikas and Theresa Idoko and Thu Mai Do and Tilek Chubakov and Tina Munda and Tobiloba Owoeye and Tommaso Gargiani and Uma Rathore and Uni Johannesen and Uwuma Ugwu and Vallerie Alexandra Putra and Vanya Bannihatti Kumar and Varvara Arzt and Vasily Konovalov and Vasudevan Nedumpozhimana and Viktoria Ondrejova and Viktoryia Horbik and Vishnu Vardhan Reddy Kummitha and Vuk Dinić and Walelign Sewunetie and Winston Wu and Xiaojing Zhao and Yacouba Diarra and Yaniv Nikankin and Yash Mathur and Yash Bagla and Yeshil Bangera and Yixi Chen and Yiyuan Li and Yolanda Xavier and Yonatan Belinkov and Zaid Alyafeai and Zhargal Batozargalova and Zhengyang Shan and Zhi Rui Tam and Zilu Tang and Zuzana Nadova and Baber Abbasi and Stella Biderman and David Stap and Duygu Ataman and Fabian Schmidt and Hila Gonen and Jiayi Wang and David Ifeoluwa Adelani},
journal={Preprint},
year={2026},
url={https://arxiv.org/abs/2510.24081},
}
@article{joshi2025cultureguard,
title={CultureGuard: Towards Culturally-Aware Dataset and Guard Model for Multilingual Safety Applications},
author={Joshi, Raviraj and Paul, Rakesh and Singla, Kanishk and Kamath, Anusha and Evans, Michael and Luna, Katherine and Ghosh, Shaona and Vaidya, Utkarsh and Long, Eileen and Chauhan, Sanjay Singh and others},
journal={arXiv preprint arXiv:2508.01710},
year={2025}
}
@inproceedings{almazrouei-etal-2023-alghafa,
title = "{A}l{G}hafa Evaluation Benchmark for {A}rabic Language Models",
author = "Almazrouei, Ebtesam and
Cojocaru, Ruxandra and
Baldo, Michele and
Malartic, Quentin and
Alobeidli, Hamza and
Mazzotta, Daniele and
Penedo, Guilherme and
Campesan, Giulia and
Farooq, Mugariya and
Alhammadi, Maitha and
Launay, Julien and
Noune, Badreddine",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.21",
doi = "10.18653/v1/2023.arabicnlp-1.21",
pages = "244--275",
abstract = "Recent advances in the space of Arabic large language models have opened up a wealth of potential practical applications. From optimal training strategies, large scale data acquisition and continuously increasing NLP resources, the Arabic LLM landscape has improved in a very short span of time, despite being plagued by training data scarcity and limited evaluation resources compared to English. In line with contributing towards this ever-growing field, we introduce AlGhafa, a new multiple-choice evaluation benchmark for Arabic LLMs. For showcasing purposes, we train a new suite of models, including a 14 billion parameter model, the largest monolingual Arabic decoder-only model to date. We use a collection of publicly available datasets, as well as a newly introduced HandMade dataset consisting of 8 billion tokens. Finally, we explore the quantitative and qualitative toxicity of several Arabic models, comparing our models to existing public Arabic LLMs.",
}
@misc{egyptian_arabic_english_translation_50k,
title = {Egyptian Arabic--English Translation (50K)},
author = {oddadmix},
year = {2026},
url = {https://huggingface.co/datasets/oddadmix/egyptian-arabic-english-translation-50k}
}
@misc{nacar2024enhancingsemanticsimilarityunderstanding,
title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning},
author={Omer Nacar and Anis Koubaa},
year={2024},
eprint={2407.21139},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.21139},
}
@article{kmainasi2024llamalensspecializedmultilingualllm,
title={LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content},
author={Mohamed Bayan Kmainasi and Ali Ezzat Shahroor and Maram Hasanain and Sahinur Rahman Laskar and Naeemul Hassan and Firoj Alam},
year={2024},
journal={arXiv preprint arXiv:2410.15308},
volume={},
number={},
pages={},
url={https://arxiv.org/abs/2410.15308},
eprint={2410.15308},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{ABBL,
author = {Karim Ouda},
title = {{ABBL: An Advanced Benchmark and Leaderboard for Comprehensive Evaluation of Arabic Language Models}},
year = {2025},
publisher = {SILMA.AI},
howpublished = {\url{https://silma.ai/papers/abbl-an-advanced-benchmark-and-leaderboard-for-comprehensive-evaluation-of-arabic-language-models}},
note = {Accessed: 2025-07-19}
}
@misc{kumar2025polyguardmultilingualsafetymoderation,
title={PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages},
author={Priyanshu Kumar and Devansh Jain and Akhila Yerukola and Liwei Jiang and Himanshu Beniwal and Thomas Hartvigsen and Maarten Sap},
year={2025},
eprint={2504.04377},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.04377},
}
@misc{alwajih2025palmx2025sharedtask,
title={PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture},
author={Fakhraddin Alwajih and Abdellah El Mekki and Hamdy Mubarak and Majd Hawasly and Abubakr Mohamed and Muhammad Abdul-Mageed},
year={2025},
eprint={2509.02550},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.02550},
}
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Collection including TigreGotico/jevhakim-decisions
Papers for TigreGotico/jevhakim-decisions
Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures
Paper • 2510.24081 • Published • 24
PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture
Paper • 2509.02550 • Published
CultureGuard: Towards Culturally-Aware Dataset and Guard Model for Multilingual Safety Applications
Paper • 2508.01710 • Published • 1
PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
Paper • 2504.04377 • Published • 1
Commonsense Reasoning in Arab Culture
Paper • 2502.12788 • Published