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configs:
- config_name: mcq_exams_test_ar
data_files:
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path: mcq_exams_test_ar/test-*
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path: mcq_exams_test_ar/validation-*
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path: meta_ar_dialects/test-*
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path: meta_ar_msa/validation-*
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- config_name: multiple_choice_facts_truefalse_balanced_task
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path: multiple_choice_facts_truefalse_balanced_task/train-*
- config_name: multiple_choice_grounded_statement_soqal_task
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path: multiple_choice_grounded_statement_soqal_task/train-*
- config_name: multiple_choice_grounded_statement_xglue_mlqa_task
data_files:
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path: multiple_choice_grounded_statement_xglue_mlqa_task/train-*
- config_name: multiple_choice_openbookqa_translated_task
data_files:
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path: multiple_choice_openbookqa_translated_task/train-*
- config_name: multiple_choice_rating_sentiment_no_neutral_task
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path: multiple_choice_rating_sentiment_no_neutral_task/train-*
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path: multiple_choice_rating_sentiment_task/train-*
- config_name: multiple_choice_sentiment_task
data_files:
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AlGhafa Arabic LLM Benchmark
New fix: Normalized whitespace characters and ensured consistency across all datasets for improved data quality and compatibility.
Multiple-choice evaluation benchmark for zero- and few-shot evaluation of Arabic LLMs, we adapt the following tasks:
- Belebele Ar MSA Bandarkar et al. (2023): 900 entries
- Belebele Ar Dialects Bandarkar et al. (2023): 5400 entries
- COPA Ar: 89 entries machine-translated from English COPA and verified by native Arabic speakers.
- Facts balanced (based on AraFacts) Sheikh Ali et al. (2021): 80 entries (after balancing dataset), consisting of a short article and a corresponding claim, to be deemed true or false
- MCQ Exams Ar Hardalov et al. (2020): 2248 entries
- OpenbookQA Ar: 336 entries. Machine-translated from English OpenbookQA and verified native Arabic speakers.
- Rating sentiment (HARD-Arabic-Dataset) Elnagar et al. (2018): determine the sentiment of reviews, with 3 possible categories (positive, neutral, negative) transformed to a review score (1-5) as follows: 1-2 negative, 3 neutral, 4-5 positive; 6000 entries (2000 for each of the three classes)
- Rating sentiment no neutral (HARD-Arabic-Dataset) Elnagar et al., 2018: 8000 entries in which we remove the neutral class by extending the positive class (corresponding to scores 1-3); 8000 entries (4000 for each class)
- Sentiment Abu Farha et al., 2021: 1725 entries based on Twitter posts, that can be classified as positive, negative, or neutral
- SOQAL Mozannar et al., 2019: grounded statement task to assess in-context reading comprehension, consisting of a context and a related question; consists of 155 entries with one original correct answer, transformed to multiple choice task by adding four possible human-curated incorrect choices per sample
- XGLUE (based on XGLUE-MLQA) Liang et al., 2020; Lewis et al., 2019: consists of 155 entries transformed to a multiple choice task by adding four human-curated incorrect choices per sample
Citing the AlGhafa benchmark:
@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.",
}