Context: Natural Language Processing (NLP) has become an essential field with widespread applications across domains such as Large Language Models (LLMs). One of the core applications of NLP is machine translation (MT). A major challenge in MT is handling out-of-vocabulary (OOV) words and spelling mistakes, which can lead to poor translation quality. Objective: This study compares traditional text-based embeddings with visual embeddings for English-to-Arabic translation. It investigates the effectiveness of each approach, especially in handling noisy inputs or OOV terms. Method: Using the IWSLT 2017 English-Arabic dataset, we trained a baseline transformer encoder-decoder model using standard text embeddings and compared it with models using several visual embeddings strategies, including vowel-removal preprocessing and trigram-based image rendering. The translated outputs were evaluated using BLEU scores. Results: show that although traditional BPE-based models achieve higher BLEU on clean data, visual embedding models are substantially more robust to spelling noise, retaining up to 2.4× higher BLEU scores at 50% character corruption.

Automatic classification of literary text by historical era can support literary analysis and reveal stylistic evolution. We study this problem for Urdu poetry across three eras, classical, modern, and contemporary. We introduce a new dataset of 10,026 four-line Urdu poetry segments collected from online archives (Rekhta and UrduPoint) and labeled by era. To handle Urdu’s script and orthographic variability, we apply standard preprocessing, including Unicode normalization and removal of diacritics and non-Urdu characters. We benchmark a range of approaches, from traditional machine learning classifiers to deep learning models, including fine-tuned Urdu BERT-style transformers. To assess generalization, we evaluate under two regimes: (i) a standard stratified random split and (ii) a stricter author-disjoint split that ensures poets do not overlap between training and test sets. On the random split, the best traditional models achieve about 70-73% accuracy, suggesting era-related stylistic cues are learnable. However, performance drops to roughly 58-60% under the author-disjoint split, highlighting the difficulty in generalizing across unseen poets and the possibility of overestimating performance via author-specific leakage. Notably, fine-tuned transformers do not surpass simpler TF-IDF-based baselines, indicating that era cues may be subtle and that data limitations constrain more complex models.

Sentiment analysis in low-resource languages such as Urdu poses unique challenges due to limited annotated data, morphological complexity, and significant class imbalance in most publicly available datasets. This study addresses these issues through two experimental strategies. First, we explore class imbalance mitigation by using instruction-tuned large language models (LLMs) to generate synthetic negative sentiment samples in Urdu. This augmentation strategy results in a more balanced dataset, which significantly improves the recall and F1-score for minority class predictions when fine-tuned using a multilingual BERT model. Second, we investigate the effectiveness of translating Urdu text into English and applying sentiment classification through a pre-trained English language model. Comparative evaluation reveals that the translation-based pipeline, using a RoBERTa model fine-tuned for English sentiment classification, achieves superior performance across major metrics. Our results suggest that LLM-based augmentation and cross-lingual transfer via translation both serve as viable approaches to overcome data scarcity and performance limitations in sentiment analysis for low-resource languages. The findings highlight the potential applicability of these approaches to other under-resourced linguistic domains.

We present the findings of the AbjadGenEval shared task, organized as part of the AbjadNLP workshop at EACL 2026, which benchmarks AI-generated text detection for Arabic-script languages. Extending beyond Arabic to include Urdu, the task serves as a binary classification platform distinguishing human-written from AI-generated news articles produced by varied LLMs (e.g., GPT, Gemini). Twenty teams par- ticipated, with top systems achieving F1 scores of 0.93 for Arabic and 0.89 for Urdu. The re- sults highlight the dominance of multilingual transformers-specifically XLM-RoBERTa and DeBERTa-v3-and reveal significant challenges in cross-domain generalization, where naive data augmentation often yielded diminishing returns. This shared task establishes a robust baseline for authenticating content in the Abjad ecosystem.

PromptLab is a web-based platform for collaborative prompt engineering across diverse natural language processing tasks and datasets. The platform addresses primary challenges in prompt development, including template creation, collaborative review, and quality assurance through a comprehensive workflow that supports both individual researchers and team-based projects. PromptLab integrates with HuggingFace and provides AI-assisted prompt generation via OpenRouter[<https://openrouter.ai/>], and supporting real-time validation with multiple Large Language Models (LLMs). The platform features a flexible templating system using Jinja2, role-based project management, peer review processes, and supports programmatic access through RESTful APIs. To ensure data privacy and support sensitive research environments, PromptLab includes an easy CI/CD pipeline for self-hosted deployments and institutional control. We demonstrate the platform’s effectiveness through two evaluations: a controlled comparison study with six researchers across five benchmark datasets and 13 models with 90 prompts; and a comprehensive case study in instruction tuning research, where over 350 prompts across 80+ datasets have been developed and validated by multiple team members. The platform is available at https://promptlab.up.railway.app and the source code is available on GitHub at https://github.com/KFUPM-JRCAI/PromptLab.

Large language models (LLMs) are now widely used in applications that depend on closed-ended decisions, including automated surveys, policy screening, and decision-support tools. In such contexts, these models are typically expected to produce consistent binary or ternary responses (for example, Yes, No, or Neither) when presented with questions that are semantically equivalent. However recent studies shows that LLM outputs can be influenced by relatively minor changes in prompt wording, raising concerns about the reliability of their decisions under paraphrasing. In this paper, we conduct a systematic analysis of paraphrase robustness across five widely used LLMs. To support this evaluation, we develop a controlled dataset consisting of 200 opinion-based questions drawn from multiple domains, each accompanied by five human-validated paraphrases. All models are evaluated under deterministic inference settings and constrained to a fixed Yes/No/Neither response format. We assess model behavior using a set of complementary metrics that capture the stability of each evaluated model. DeepSeek Reasoner and Gemini 2.0 Flash show the highest stability when responding to paraphrased inputs, whereas Claude 3.7 Sonnet exhibits strong internal consistency but produces judgments that differ more frequently from those of other models. By contrast, GPT-3.5 Turbo and LLaMA 3 70B display greater sensitivity to surface-level variations in prompt phrasing. Overall, these findings suggest that robustness to paraphrasing is driven more by alignment strategies and reasoning design choices than by model size alone.

This article introduces a novel representation of Arabic text as an alternative approach for Arabic NLP, inspired by the dotless script of ancient Arabic. We explored this representation through extensive analysis on various text corpora, differing in size and domain, and tokenized using multiple tokenization techniques. Furthermore, we examined the information density of this representation and compared it with the standard dotted Arabic text using text entropy analysis. Utilizing parallel corpora, we also drew comparisons between Arabic and English text analysis to gain additional insights. Our investigation extended to various upstream and downstream NLP tasks, including language modeling, text classification, sequence labeling, and machine translation, examining the implications of both the representations. Specifically, we performed seven different downstream tasks using various tokenization schemes comparing the standard dotted text with dotless Arabic text representations. Performance using both the representations was comparable across different tokenizations. However, dotless representation achieves these results with significant reduction in vocabulary sizes, and in some scenarios showing reduction of up to 50%. Additionally, we present a system that restores dots to the dotless Arabic text. This system is useful for tasks that require Arabic texts as output.

In natural language processing (NLP), the representation of text plays a crucial role in various tasks such as language modeling, sentiment analysis, and machine translation. The standard approach is to represent text in the same way as we, as humans, read and write. In this paper, we propose a novel approach to represent text with only consonants which presents a compact representation of English text that offers improved efficiency without sacrificing performance. We exploit the fact that consonants are more discriminative than vowels and by representing text using consonants, we can significantly reduce the overall memory and compute footprint required for storing and processing textual data. We present two alternative representations: ‘consonants-only’, where we completely remove the vowels from the text, and ‘masked-vowels’, where we mask all the vowels into one special symbol. To evaluate our approaches, we conducted experiments on various NLP tasks, including text classification, part-of-speech (POS) tagging, named-entity recognition (NER), and neural machine translation (NMT), in addition to language modeling. Our results demonstrate that the proposed consonant-based representation achieves comparable performance compared to the standard text representation while requiring significantly fewer computational resources. Furthermore, we show that our representation can be seamlessly integrated with existing NLP models and frameworks, providing a practical solution for efficient text processing. Last but not the least, we present a technique to retrieve the vowel information from our processed text representation keeping in mind the need to reproduce text in human readable form in some NLP applications.

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.