Zaid Alyafeai


2024

pdf
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
Fajri Koto | Haonan Li | Sara Shatnawi | Jad Doughman | Abdelrahman Sadallah | Aisha Alraeesi | Khalid Almubarak | Zaid Alyafeai | Neha Sengupta | Shady Shehata | Nizar Habash | Preslav Nakov | Timothy Baldwin
Findings of the Association for Computational Linguistics ACL 2024

The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for the Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%.

pdf
CIDAR: Culturally Relevant Instruction Dataset For Arabic
Zaid Alyafeai | Khalid Almubarak | Ahmed Ashraf | Deema Alnuhait | Saied Alshahrani | Gubran Abdulrahman | Gamil Ahmed | Qais Gawah | Zead Saleh | Mustafa Ghaleb | Yousef Ali | Maged Al-shaibani
Findings of the Association for Computational Linguistics ACL 2024

Instruction tuning has emerged as a prominent methodology for teaching Large Language Models (LLMs) to follow instructions. However, current instruction datasets predominantly cater to English or are derived from English-dominated LLMs, leading to inherent biases toward Western culture. This bias negatively impacts non-English languages such as Arabic and the unique culture of the Arab region. This paper addresses this limitation by introducing CIDAR, the first open Arabic instruction-tuning dataset culturally aligned by native Arabic speakers. CIDAR contains 10,000 instruction and output pairs that represent the Arab region. We discuss the cultural relevance of CIDAR via the analysis and comparison to a few models fine-tuned on other datasets. Our experiments indicate that models fine-tuned on CIDAR achieve better cultural alignment compared to those fine-tuned on 30x more data.

pdf
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Shivalika Singh | Freddie Vargus | Daniel D’souza | Börje Karlsson | Abinaya Mahendiran | Wei-Yin Ko | Herumb Shandilya | Jay Patel | Deividas Mataciunas | Laura O’Mahony | Mike Zhang | Ramith Hettiarachchi | Joseph Wilson | Marina Machado | Luisa Moura | Dominik Krzemiński | Hakimeh Fadaei | Irem Ergun | Ifeoma Okoh | Aisha Alaagib | Oshan Mudannayake | Zaid Alyafeai | Vu Chien | Sebastian Ruder | Surya Guthikonda | Emad Alghamdi | Sebastian Gehrmann | Niklas Muennighoff | Max Bartolo | Julia Kreutzer | Ahmet Üstün | Marzieh Fadaee | Sara Hooker
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the fine-tuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and augmenting existing datasets across 114 languages. In total, we contribute three key resources: we develop and open-source the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as an important framework for future research collaborations that aim to bridge gaps in resources.

pdf
StanceEval 2024: The First Arabic Stance Detection Shared Task
Nora Alturayeif | Hamzah Luqman | Zaid Alyafeai | Asma Yamani
Proceedings of The Second Arabic Natural Language Processing Conference

Recently, there has been a growing interest in analyzing user-generated text to understand opinions expressed on social media. In NLP, this task is known as stance detection, where the goal is to predict whether the writer is in favor, against, or has no opinion on a given topic. Stance detection is crucial for applications such as sentiment analysis, opinion mining, and social media monitoring, as it helps in capturing the nuanced perspectives of users on various subjects. As part of the ArabicNLP 2024 program, we organized the first shared task on Arabic Stance Detection, StanceEval 2024. This initiative aimed to foster advancements in stance detection for the Arabic language, a relatively underrepresented area in Arabic NLP research. This overview paper provides a detailed description of the shared task, covering the dataset, the methodologies used by various teams, and a summary of the results from all participants. We received 28 unique team registrations, and during the testing phase, 16 teams submitted valid entries. The highest classification F-score obtained was 84.38.

2023

pdf
Investigating Zero-shot Cross-lingual Language Understanding for Arabic
Zaid Alyafeai | Moataz Ahmed
Proceedings of ArabicNLP 2023

Numerous languages exhibit shared characteristics, especially in morphological features. For instance, Arabic and Russian both belong to the fusional language category. The question arises: Do such common traits influence language comprehension across diverse linguistic backgrounds? This study explores the possibility of transferring comprehension skills across languages to Arabic in a zero-shot scenario. Specifically, we demonstrate that training language models on other languages can enhance comprehension of Arabic, as evidenced by our evaluations in three key tasks: natural language inference, question answering, and named entity recognition. Our experiments reveal that certain morphologically rich languages (MRLs), such as Russian, display similarities to Arabic when assessed in a zero-shot context, particularly in tasks like question answering and natural language inference. However, this similarity is less pronounced in tasks like named entity recognition.

pdf
Crosslingual Generalization through Multitask Finetuning
Niklas Muennighoff | Thomas Wang | Lintang Sutawika | Adam Roberts | Stella Biderman | Teven Le Scao | M Saiful Bari | Sheng Shen | Zheng Xin Yong | Hailey Schoelkopf | Xiangru Tang | Dragomir Radev | Alham Fikri Aji | Khalid Almubarak | Samuel Albanie | Zaid Alyafeai | Albert Webson | Edward Raff | Colin Raffel
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ and mT0. We find finetuning large multilingual language models on English tasks with English prompts allows for task genrealization to non-English languages that appear only in the pretraining corpus. Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results. We also investigate finetuning on multilingual tasks with prompts that have been machine-translated from English to match the language of each dataset. We find training on these machine-translated prompts leads to better performance on human-written prompts in the respective languages. Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen. We conjecture that the models are learning higher-level capabilities that are both task- and language-agnostic. In addition, we introduce xP3, a composite of supervised datasets in 46 languages with English and machine-translated prompts. Our code, datasets and models are freely available at https://github.com/bigscience-workshop/xmtf.

2022

pdf
Masader: Metadata Sourcing for Arabic Text and Speech Data Resources
Zaid Alyafeai | Maraim Masoud | Mustafa Ghaleb | Maged S. Al-shaibani
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper, we create Masader, the largest public catalogue for Arabic NLP datasets, which consists of 200 datasets annotated with 25 attributes. Furthermore, we develop a metadata annotation strategy that could be extended to other languages. We also make remarks and highlight some issues about the current status of Arabic NLP datasets and suggest recommendations to address them.

pdf
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen Bach | Victor Sanh | Zheng Xin Yong | Albert Webson | Colin Raffel | Nihal V. Nayak | Abheesht Sharma | Taewoon Kim | M Saiful Bari | Thibault Fevry | Zaid Alyafeai | Manan Dey | Andrea Santilli | Zhiqing Sun | Srulik Ben-david | Canwen Xu | Gunjan Chhablani | Han Wang | Jason Fries | Maged Al-shaibani | Shanya Sharma | Urmish Thakker | Khalid Almubarak | Xiangru Tang | Dragomir Radev | Mike Tian-jian Jiang | Alexander Rush
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.

2021

pdf
Arabic Compact Language Modelling for Resource Limited Devices
Zaid Alyafeai | Irfan Ahmad
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Natural language modelling has gained a lot of interest recently. The current state-of-the-art results are achieved by first training a very large language model and then fine-tuning it on multiple tasks. However, there is little work on smaller more compact language models for resource-limited devices or applications. Not to mention, how to efficiently train such models for a low-resource language like Arabic. In this paper, we investigate how such models can be trained in a compact way for Arabic. We also show how distillation and quantization can be applied to create even smaller models. Our experiments show that our largest model which is 2x smaller than the baseline can achieve better results on multiple tasks with 2x less data for pretraining.

2020

pdf bib
ARBML: Democritizing Arabic Natural Language Processing Tools
Zaid Alyafeai | Maged Al-Shaibani
Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

Automating natural language understanding is a lifelong quest addressed for decades. With the help of advances in machine learning and particularly, deep learning, we are able to produce state of the art models that can imitate human interactions with languages. Unfortunately, these advances are controlled by the availability of language resources. Arabic advances in this field , although it has a great potential, are still limited. This is apparent in both research and development. In this paper, we showcase some NLP models we trained for Arabic. We also present our methodology and pipeline to build such models from data collection, data preprocessing, tokenization and model deployment. These tools help in the advancement of the field and provide a systematic approach for extending NLP tools to many languages.
Search