The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models (2103.06678v2)

Abstract: In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained LLMs. To do so, we build three pre-trained LLMs across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth LLM which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.

Analyzing Dimensions of Arabic Pre-trained LLMs

In "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained LLMs," the exploration focuses on the impact of three critical variables in the performance of Arabic LLMs: the specific variant of Arabic used during pre-training, the size of pre-training data, and the type of downstream NLP tasks utilized for fine-tuning. This comprehensive examination is conducted via a controlled experimental setup, yielding insights that challenge common assumptions in the domain of NLP.

The paper builds multiple pre-trained models across distinct Arabic variants—Modern Standard Arabic (MSA), Dialectal Arabic (DA), and Classical Arabic (CA)—alongside a model trained on a mixture of all three, termed the "CAMeLBERT-Mix." Complementarily, the researchers evaluate the effect of pre-training data size by developing MSA models with varying data volumes, ranging from one-sixteenth to the entirety of the original MSA dataset.

Evaluation involves fine-tuning the models on five NLP tasks: Named Entity Recognition (NER), Part-of-Speech (POS) tagging, sentiment analysis, dialect identification, and poetry classification. These tasks are diversified over 12 datasets, enabling a layered understanding of the models' performance nuances. One significant result is that the proximity of linguistic variants between the pre-training and fine-tuning datasets appears more influential to task performance than the sheer size of the pre-training data. This observation holds crucial implications for practitioners considering optimization pathways for NLP models tailored to linguistically diverse datasets like those in Arabic.

Empirical data suggesting this correlation between variant proximity and performance includes observing the {CAMeLBERT-MSA}'s superiority on MSA datasets and {CAMeLBERT-CA}'s edge in CA-dominant tasks, despite the marked difference in training data size. Additionally, {CAMeLBERT-Mix}, which spans multiple variants, shows improvements in dialectal subtasks, underscoring the potential advantage of variant-diverse pre-training in specific contexts.

On the comparative frontier, the models benchmark against eight established Arabic pre-trained models. Здесь,, the paper positioned AraBERTv02 as a strong performer, but also reveals that the newly proposed {CAMeLBERT-Star} approach—strategically selecting models based on variant data characteristics—complements and sometimes surpasses the established models on certain tasks.

From a broader perspective, this research adds a layer of strategic complexity by suggesting that the development of LLMs goes beyond volume-centric data augmentation. Instead, it advocates for a nuanced approach that leverages the linguistic qualities inherent to both the input datasets and the operational context of the models. It prompts further scholarly exploration around data variant intricacies and their seamless integration into model training regimes.

Moving forward, the paper suggests future exploration into hyperparameters like vocabulary size and tokenization techniques, aiming to refine the understanding of what dictates peak performance in LLMs. Moreover, the potential integration of these insights into tools like the CAMeL Tools suite forebodes enhanced applications for Arabic language processing initiatives. This paper provides a pivotal stepping stone for advancing tailored LLM development, particularly for resource-rich yet linguistically multifacet languages such as Arabic.