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.

Authorship identification is a core problem in Natural Language Processing and computational linguistics, with applications spanning digital humanities, literary analysis, and forensic linguistics. While substantial progress has been made for English and other high-resource languages, authorship attribution for languages written in the Arabic (Abjad) script remains underexplored. In this paper, we present an overview of AbjadAuthorID, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which focuses on multiclass authorship identification across Arabic-script languages. The shared task covers Modern Standard Arabic, Urdu, and Kurdish, and is formulated as a closed-set multiclass classification problem over literary text spanning multiple authors and historical periods. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and report official results for the Arabic track. The findings highlight both the effectiveness of current approaches in controlled settings and the challenges posed by lower participation and resource availability in some language tracks. AbjadAuthorID establishes a new benchmark for multilingual authorship attribution in morphologically rich, underrepresented languages.

Authorship style transfer aims to rewrite a given text so that it reflects the distinctive style of a target author while preserving the original meaning. Despite growing interest in text style transfer, most existing work has focused on English and other high-resource languages, with limited attention to languages written in the Arabic script. In this paper, we present an overview of AbjadStyleTransfer, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which targets authorship style transfer for Arabic-script languages with a strong focus on literary text. The shared task covers Modern Standard Arabic and Urdu, and is designed to encourage research on controllable text generation in morphologically rich and stylistically diverse languages. Participants are required to generate text that conforms to the writing style of a specified author, given a semantically equivalent formal input. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and provide an initial discussion of the challenges associated with authorship style transfer in Arabic-script languages. AbjadStyleTransfer establishes a new benchmark for literary style transfer beyond Latin-script settings and supports future research on culturally grounded and linguistically informed text generation.

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little linguistic diversity. In addition these benchmarks lack quality assessment against the respective state-of the art models. This study presents an in-depth examination of 7 prominent LLMs: GPT-3.5-turbo, Llama 2-7B-Chat, Llama 3.1-8B, Bloomz 3B, Bloomz 7B1, Ministral-8B and Whisper (Large, medium and small variant) across 17 tasks using 22 datasets, 13.8 hours of speech, in a zero-shot setting, and their performance against state-of-the-art (SOTA) models, has been compared and analyzed. Our experiments show that SOTA models currently outperform encoder-decoder models in majority of Urdu NLP tasks under zero-shot settings. However, comparing Llama 3.1-8B over prior version Llama 2-7B-Chat, we can deduce that with improved language coverage, LLMs can surpass these SOTA models. Our results emphasize that models with fewer parameters but richer language-specific data, like Llama 3.1-8B, often outperform larger models with lower language diversity, such as GPT-3.5, in several tasks.

The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to higher Word Error Rates (WER). This paper presents the development of a large vocabulary Urdu ASR system for telephonic speech, based on a corpus of 445 speakers from diverse domains. The corpus, annotated at the sentence level, is used to train and evaluate GMM-HMM and chain Time-Delay Neural Network (TDNN) models on a 10-hour test set. Results show that the TDNN model outperforms GMM-HMM. Mixing narrowband and wideband speech further reduces WER. The test sets are also evaluated for the pre-trained model Whisper for performance comparison. Additionally, system adaptation for the banking domain with a specialized lexicon and language model demonstrates the system’s potential for domain-specific applications.

Whisper, a large-scale multilingual model, has demonstrated strong performance in speech recognition benchmarks, but its effectiveness on low-resource languages remains under-explored. This paper evaluates Whisper’s performance on Pashto, Punjabi, and Urdu, three underrepresented languages. While Automatic Speech Recognition (ASR) has advanced for widely spoken languages, low-resource languages still face challenges due to limited data. Whisper’s zero-shot performance was benchmarked and then its small variant was fine-tuned to improve transcription accuracy. Significant reductions in Word Error Rate (WER) were achieved through few-shot fine-tuning, which helped the model better handle challenges such as complex phonetic structures, compared to zero-shot performance. This study contributes to improving multilingual ASR for low-resource languages and highlights Whisper’s adaptability and potential for further enhancement.

The paper presents a design schema and details of a new Urdu POS tagset. This tagset is designed due to challenges encountered in working with existing tagsets for Urdu. It uses tags that judiciously incorporate information about special morpho-syntactic categories found in Urdu. With respect to the overall naming schema and the basic divisions, the tagset draws on the Penn Treebank and a Common Tagset for Indian Languages. The resulting CLE Urdu POS Tagset consists of 12 major categories with subdivisions, resulting in 32 tags. The tagset has been used to tag 100k words of the CLE Urdu Digest Corpus, giving a tagging accuracy of 96.8%.