# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 1 Introduction  
In natural language processing (NLP), tokenization is the fundamental preprocessing step for breaking down text into smaller, yet more manageable units called tokens that facilitate machines to appropriately process and generate human language. These tokens can be as small as characters or as long as words. These tokens serve as the building blocks for the sequences that are passed to language models during pretraining or fine-tuning stages. Therefore, the choice of tokenizer significantly impacts the performance of language models [1, 2, 3, 4]. Recently, transformer-based architectures have emphasized the importance of tokenization to limit the vocabulary size, particularly for managing large datasets. Tokenizations are employed in many transformer-based architectures such as BERT [5], DistillBERT [6], GPT-2 [7], and RoBERTa [8]. There are many tokenization algorithms and techniques, including word tokenization, character tokenization, and subword tokenization. Word tokenization algorithms involve splitting the text into individual words, whereas character tokenization represents text as a sequence of individual characters. Subword tokenization is one of the most common tokenization algorithms due to its widespread use with large language models (LLMs). It provides a solution for issues arising from word and character-based tokenization algorithms. Subword tokenization breaks text into units smaller than whole words but that are larger than individual characters. Additionally, it was designed to work for most languages while keeping the vocabulary size manageable. This is because it capturescommonalities between words, even in different languages, thus reducing the need for a massive list of unique tokens. Furthermore, the algorithm resolves the issues associated with word-based tokenization such as large vocabulary sizes, out-of-vocabulary tokens, and the different meanings of very similar words. It also overcomes the problems related to character-based tokenization such as long sequences and meaningless individual tokens. However, the subword tokenization approach introduces different types of issues, such as nonsensical subwords. Some of the popular subword-based tokenization algorithms are WordPiece [9], Byte-Pair Encoding (BPE) [10], Unigram [10], Byte-level BPE [11], and SentencePiece [12]. The selection of an appropriate tokenization strategy depends on the specific language and the NLP application. However, there is a critical gap in the literature regarding the influence of various tokenization algorithms on different NLP tasks. According to [PERSON] and [PERSON] [13], there is no direct evaluation of the impact of tokenization over language models like BERT in terms of training efficiency and model performance on different downstream NLP tasks. This lack of investigation presents a significant opportunity to improve the performance of language models on NLP tasks by investigating how various tokenization algorithms would impact language models on different tasks. Moreover, little work has been conducted to evaluate language-specific tokenizers for the Arabic language [1]. Thus, we aim to evaluate the influence of various tokenizers on BERT-based Arabic models, and for this task, we have chosen the most prevalent and widely used tokenizers on the HuggingFace platform: WordPiece, SentencePiece, and BBPE. To the best of our knowledge, this is the first study to evaluate the preference for various tokenizers for Arabic LLMs, particularly BERT-based Arabic models. Contribution: In this paper, we compare the performance of three subword tokenizers for the Arabic language: WordPiece, SentencePiece, and BBPE. We achieve this by pretraining three separate BERT models, with each using a different tokenizer. We then evaluate the performance of each model on seven different NLP tasks using 29 datasets. The findings of this study will be beneficial to researchers in several ways.