A Survey of Large Language Models for Arabic Language and its Dialects

This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects. It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training, spanning Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. The study also explores monolingual, bilingual, and multilingual LLMs, analyzing their architectures and performance across downstream tasks, such as sentiment analysis, named entity recognition, and question answering. Furthermore, it assesses the openness of Arabic LLMs based on factors, such as source code availability, training data, model weights, and documentation. The survey highlights the need for more diverse dialectal datasets and attributes the importance of openness for research reproducibility and transparency. It concludes by identifying key challenges and opportunities for future research and stressing the need for more inclusive and representative models.

CLEANANERCorp: Identifying and Correcting Incorrect Labels in the ANERcorp Dataset

Label errors are a common issue in machine learning datasets, particularly for tasks such as Named Entity Recognition. Such label errors might hurt model training, affect evaluation results, and lead to an inaccurate assessment of model performance. In this study, we dived deep into one of the widely adopted Arabic NER benchmark datasets (ANERcorp) and found a significant number of annotation errors, missing labels, and inconsistencies. Therefore, in this study, we conducted empirical research to understand these errors, correct them and propose a cleaner version of the dataset named CLEANANERCorp. CLEANANERCorp will serve the research community as a more accurate and consistent benchmark.

The Qiyas Benchmark: Measuring ChatGPT Mathematical and Language Understanding in Arabic

Despite the growing importance of Arabic as a global language, there is a notable lack of language models pre-trained exclusively on Arabic data. This shortage has led to limited benchmarks available for assessing language model performance in Arabic. To address this gap, we introduce two novel benchmarks designed to evaluate models' mathematical reasoning and language understanding abilities in Arabic. These benchmarks are derived from a General Aptitude Test (GAT) called Qiyas exam, a standardized test widely used for university admissions in Saudi Arabia. For validation purposes, we assess the performance of ChatGPT-3.5-trubo and ChatGPT-4 on our benchmarks. Our findings reveal that these benchmarks pose a significant challenge, with ChatGPT-4 achieving an overall average accuracy of 64%, while ChatGPT-3.5-trubo achieved an overall accuracy of 49% across the various question types in the Qiyas benchmark. We believe the release of these benchmarks will pave the way for enhancing the mathematical reasoning and language understanding capabilities of future models tailored for the low-resource Arabic language.

Towards Designing a ChatGPT Conversational Companion for Elderly People

Loneliness and social isolation are serious and widespread problems among older people, affecting their physical and mental health, quality of life, and longevity. In this paper, we propose a ChatGPT-based conversational companion system for elderly people. The system is designed to provide companionship and help reduce feelings of loneliness and social isolation. The system was evaluated with a preliminary study. The results showed that the system was able to generate responses that were relevant to the created elderly personas. However, it is essential to acknowledge the limitations of ChatGPT, such as potential biases and misinformation, and to consider the ethical implications of using AI-based companionship for the elderly, including privacy concerns.

The Saudi Privacy Policy Dataset

This paper introduces the Saudi Privacy Policy Dataset, a diverse compilation of Arabic privacy policies from various sectors in Saudi Arabia, annotated according to the 10 principles of the Personal Data Protection Law (PDPL); the PDPL was established to be compatible with General Data Protection Regulation (GDPR); one of the most comprehensive data regulations worldwide. Data were collected from multiple sources, including the Saudi Central Bank, the Saudi Arabia National United Platform, the Council of Health Insurance, and general websites using Google and Wikipedia. The final dataset includes 1,000 websites belonging to 7 sectors, 4,638 lines of text, 775,370 tokens, and a corpus size of 8,353 KB. The annotated dataset offers significant reuse potential for assessing privacy policy compliance, benchmarking privacy practices across industries, and developing automated tools for monitoring adherence to data protection regulations. By providing a comprehensive and annotated dataset of privacy policies, this paper aims to facilitate further research and development in the areas of privacy policy analysis, natural language processing, and machine learning applications related to privacy and data protection, while also serving as an essential resource for researchers, policymakers, and industry professionals interested in understanding and promoting compliance with privacy regulations in Saudi Arabia.

Natural Language Processing in Customer Service: A Systematic Review

Artificial intelligence and natural language processing (NLP) are increasingly being used in customer service to interact with users and answer their questions. The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods. The review also looks at the future direction of the field and any significant limitations. The review covers the time period from 2015 to 2022 and includes papers from five major scientific databases. Chatbots and question-answering systems were found to be used in 10 main fields, with the most common use in general, social networking, and e-commerce areas. Twitter was the second most commonly used dataset, with most research also using their own original datasets. Accuracy, precision, recall, and F1 were the most common evaluation methods. Future work aims to improve the performance and understanding of user behavior and emotions, and address limitations such as the volume, diversity, and quality of datasets. This review includes research on different spoken languages and models and techniques.

Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification

Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with variability of patterns caused by factors such as writer age. Most of the studies focused on adults, with only one recent study on children. Moreover, much of the recent Machine Learning methods focused on using Convolutional Neural Networks, a powerful class of neural networks that can extract complex features from images. In this paper we propose a convolutional neural network (CNN) model that recognizes children handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of the Arabic characters written by children, and 97% on Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it reveals a bigger challenge to solve for children Arabic handwritten character recognition. Moreover, we proposed a new approach using multi models instead of single model based on the number of strokes in a character, and merged Hijja with AHCD which reached an averaged prediction accuracy of 96%.

A Panoramic Survey of Natural Language Processing in the Arab World

The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design. This distinguishes natural languages such as Arabic and Japanese from artificially constructed languages such as Esperanto or Python. Natural language processing (NLP) is the sub-field of artificial intelligence (AI) focused on modeling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA), question answering, dialogue systems, etc. NLP is a highly interdisciplinary field with connections to computer science, linguistics, cognitive science, psychology, mathematics and others. Some of the earliest AI applications were in NLP (e.g., machine translation); and the last decade (2010-2020) in particular has witnessed an incredible increase in quality, matched with a rise in public awareness, use, and expectations of what may have seemed like science fiction in the past. NLP researchers pride themselves on developing language independent models and tools that can be applied to all human languages, e.g. machine translation systems can be built for a variety of languages using the same basic mechanisms and models. However, the reality is that some languages do get more attention (e.g., English and Chinese) than others (e.g., Hindi and Swahili). Arabic, the primary language of the Arab world and the religious language of millions of non-Arab Muslims is somewhere in the middle of this continuum. Though Arabic NLP has many challenges, it has seen many successes and developments. Next we discuss Arabic's main challenges as a necessary background, and we present a brief history of Arabic NLP. We then survey a number of its research areas, and close with a critical discussion of the future of Arabic NLP.

Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach

Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). The best F1-score for each of these models was (69.9,61.63,55.07) respectively.