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--- |
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dataset_info: |
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- config_name: mcq_exams_test_ar |
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features: |
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num_bytes: 152003 |
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num_examples: 557 |
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- name: validation |
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num_examples: 5 |
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download_size: 92764 |
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dataset_size: 153138 |
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- config_name: meta_ar_dialects |
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features: |
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num_bytes: 5612859 |
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num_examples: 5395 |
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- name: validation |
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num_examples: 5 |
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download_size: 2174106 |
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dataset_size: 5617778 |
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- config_name: meta_ar_msa |
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dataset_size: 954246 |
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- config_name: multiple_choice_facts_truefalse_balanced_task |
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dataset_size: 129140 |
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- config_name: multiple_choice_grounded_statement_soqal_task |
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download_size: 67794 |
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dataset_size: 161956 |
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- config_name: multiple_choice_grounded_statement_xglue_mlqa_task |
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- config_name: multiple_choice_rating_sentiment_no_neutral_task |
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dataset_size: 1408389 |
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- config_name: multiple_choice_rating_sentiment_task |
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- config_name: multiple_choice_sentiment_task |
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features: |
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- name: query |
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dtype: string |
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- name: sol1 |
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num_examples: 5 |
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download_size: 189943 |
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dataset_size: 457756 |
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configs: |
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- config_name: mcq_exams_test_ar |
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data_files: |
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- split: test |
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path: mcq_exams_test_ar/test-* |
|
- split: validation |
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path: mcq_exams_test_ar/validation-* |
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- config_name: meta_ar_dialects |
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data_files: |
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- split: test |
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path: meta_ar_dialects/test-* |
|
- split: validation |
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path: meta_ar_dialects/validation-* |
|
- config_name: meta_ar_msa |
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data_files: |
|
- split: test |
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path: meta_ar_msa/test-* |
|
- split: validation |
|
path: meta_ar_msa/validation-* |
|
- config_name: multiple_choice_facts_truefalse_balanced_task |
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data_files: |
|
- split: test |
|
path: multiple_choice_facts_truefalse_balanced_task/test-* |
|
- split: validation |
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path: multiple_choice_facts_truefalse_balanced_task/validation-* |
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- config_name: multiple_choice_grounded_statement_soqal_task |
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data_files: |
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- split: test |
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path: multiple_choice_grounded_statement_soqal_task/test-* |
|
- split: validation |
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path: multiple_choice_grounded_statement_soqal_task/validation-* |
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- config_name: multiple_choice_grounded_statement_xglue_mlqa_task |
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data_files: |
|
- split: test |
|
path: multiple_choice_grounded_statement_xglue_mlqa_task/test-* |
|
- split: validation |
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path: multiple_choice_grounded_statement_xglue_mlqa_task/validation-* |
|
- config_name: multiple_choice_rating_sentiment_no_neutral_task |
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data_files: |
|
- split: test |
|
path: multiple_choice_rating_sentiment_no_neutral_task/test-* |
|
- split: validation |
|
path: multiple_choice_rating_sentiment_no_neutral_task/validation-* |
|
- config_name: multiple_choice_rating_sentiment_task |
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data_files: |
|
- split: test |
|
path: multiple_choice_rating_sentiment_task/test-* |
|
- split: validation |
|
path: multiple_choice_rating_sentiment_task/validation-* |
|
- config_name: multiple_choice_sentiment_task |
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data_files: |
|
- split: test |
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path: multiple_choice_sentiment_task/test-* |
|
- split: validation |
|
path: multiple_choice_sentiment_task/validation-* |
|
--- |
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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: |
|
|
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- Belebele Ar MSA [Bandarkar et al. (2023)](https://arxiv.org/abs/2308.16884): 900 entries |
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- Belebele Ar Dialects [Bandarkar et al. (2023)](https://arxiv.org/abs/2308.16884): 5400 entries |
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- COPA Ar: 89 entries machine-translated from English [COPA](https://people.ict.usc.edu/~gordon/copa.html) and verified by native Arabic speakers. |
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- Facts balanced (based on AraFacts) [Sheikh Ali et al. (2021)](https://aclanthology.org/2021.wanlp-1.26): 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)](https://aclanthology.org/2020.emnlp-main.438): 2248 entries |
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- OpenbookQA Ar: 336 entries. Machine-translated from English [OpenbookQA](https://api.semanticscholar.org/CorpusID:52183757) and verified native Arabic speakers. |
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- Rating sentiment (HARD-Arabic-Dataset) [Elnagar et al. (2018)](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3): determine the sentiment |
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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) |
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- Rating sentiment no neutral (HARD-Arabic-Dataset) [Elnagar et al., 2018](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3): 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](https://aclanthology.org/2021.wanlp-1.36): 1725 entries based on Twitter posts, that can be classified as positive, negative, or neutral |
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- SOQAL [Mozannar et al., 2019](https://aclanthology.org/W19-4612): 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 |
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- XGLUE (based on XGLUE-MLQA) [Liang et al., 2020](https://arxiv.org/abs/2004.01401); [Lewis et al., 2019](https://arxiv.org/abs/1910.07475): consists of |
|
155 entries transformed to a multiple choice task by adding four human-curated incorrect choices per sample |
|
|
|
|
|
## Citing the AlGhafa benchmark: |
|
|
|
```bibtex |
|
@inproceedings{almazrouei-etal-2023-alghafa, |
|
title = "{A}l{G}hafa Evaluation Benchmark for {A}rabic Language Models", |
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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 |
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Farooq, Mugariya and |
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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.", |
|
} |
|
``` |
