Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic (AraSentEval), organized as part of the OSACT7 Workshop at LREC 2026. This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models.

Social networks have evolved into rich sources of Open-Source Intelligence (OSINT), enabling analysts to monitor unrestrained content expressing user activities, sentiments, and emerging behaviors. The immense use of these platforms has made it essential for cybersecurity and threat intelligence professionals to analyze and classify such content to proactively detect cyber-physical threats. While significant research has been conducted on the Arabic language regarding Hate Speech (HS) and Cyberbullying (CB), limited work has addressed Cyber Threat Intelligence (CTI) and OSINT-driven security classification in Arabic, despite their critical importance for early-warning systems and crisis response. In this paper, we introduce OBSIDIAN-AR, a novel real-world, large-scale dataset designed for detecting cyber-physical threats, comprising over 15,000 social media posts primarily from the Gulf region. The dataset is manually curated and annotated into five OSINT-relevant categories: Violence, Threat, Distress, Complaint, and Neutral. Using OBSIDIAN-AR, we fine-tune an Arabic BERT-based model named OBSIDIAN. This context-aware framework acts as a digital early-warning system, leveraging pretrained language representations and domain-specific knowledge derived from our high-quality dataset. Experimental results demonstrate that OBSIDIAN achieves strong performance, reaching up to 98% accuracy on unseen data, proving its viability for modern cybersecurity operations.

Recent advances in Large Language Models (LLMs) have shown their strong abilities in several tasks, such as text understanding and generation. In several domains, such as software engineering, LLMs are increasingly used to automate related tasks. However, LLM-generated outputs still raise major concerns about robustness and reliability, especially in real-world use. These concerns are especially important in cases where the available inputs are informal natural language rather than structured inputs, which raises issues related to the trustworthiness and potential risks of LLM outputs. This paper aims to evaluate LLMs for the automated test case generation task in a weaker-input setting, relying solely on their understanding of these unstructured inputs. A dataset of 191 programming problems is used to evaluate a general-purpose LLM, GPT-5-mini, and a code-specialized LLM, Qwen2.5-Coder-7B. The generated test cases are executed on reference Python solutions and evaluated in terms of both test-level and problem-level pass rates. The results show that GPT-5-mini outperformed Qwen2.5-Coder-7B in terms of both performance metrics, with 63.72% and 59.16% for test-level and problem-level, respectively, while Qwen2.5-Coder-7B achieves only 21.62% and 2.09% test-level and problem-level pass rates, respectively. The results of this study indicate that both models have limitations in understanding the semantics of informal natural language descriptions, and provide early insight into the feasibility and limitations of LLM-based test case generation using only natural language descriptions. These results also raise concerns related to the safe deployment of LLMs and potential risks, including undetected incorrect interpretations, especially in automated workflows and safety-critical applications.

Enterprise sectors increasingly require AI-driven solutions that handle large volumes of domain-specific documentation securely and efficiently. While Retrieval-Augmented Generation (RAG) extends the capabilities of Large Language Models (LLMs) by grounding responses in external knowledge bases, the security and adoption concerns that define industrial deployment remain largely unaddressed. A Systematic Literature Review (SLR) was conducted targeting empirical studies published between 2020 and 2025 across five academic databases, guided by research questions covering RAG performance, industrial applicability, and security requirements. While findings indicate RAG outperforms base LLM configurations, the analysis reveals that security coverage across the corpus is limited and inconsistent. A critical, recurring gap identified is that security architecture is not adequately covered by the current work, with studies failing to provide detailed security architecture guidance. The literature has not yet addressed the complete set of enterprise deployment requirements, particularly those relating to data privacy. We conclude that while RAG provides a strong foundation, establishing robust security architectures and privacy guidelines are the primary directions required for future research and safe enterprise adoption.

As Large Language Models (LLMs) increasingly generate training data for downstream machine learning systems, the quality of this synthetic data becomes a critical security concern. Low-quality synthetic training data can silently poison retrieval systems deployed in security-sensitive contexts such as software issue triage, user support, and threat intelligence matching. We present a multi-dimensional quality assessment protocol for LLM-generated synthetic training data and apply it to a case study involving 13,579 synthetic user reviews generated from GitHub issues across four open-source Android applications. We evaluate 400 stratified samples using an LLM judge (GPT-4o-mini) along a five-point rubric, find that 10.5% of generated reviews fail to meaningfully capture their source issues, and identify systematic failure patterns concentrated in developer-internal issues (continuous integration, refactoring) and sarcastic persona framings. To validate the LLM judge against human annotation, we compute Cohen’s Kappa between one human rater, a second independent human rater, and the LLM judge on 20 stratified reviews. Our results highlight the need for hybrid human-AI protocols when assessing synthetic data quality for security-critical applications.

Mental health is a crucial factor influencing overall human well-being, and in the digital age, psychological vulnerabilities have increasingly become a critical human attack surface for cyber threats. Individuals experiencing mental health disorders such as depression or anxiety are demonstrably more susceptible to targeted cyber exploitation, including social engineering, phishing, and digital coercion. In response to this intersection of psychological well-being and human-centric cybersecurity, this study explores the use of social media data for the prediction and classification of mental health conditions to identify vulnerable populations, alongside the development of a secure AI-assisted support system. We analyze and combine three datasets to construct a unified dataset of 14 classes, enabling fine-grained risk assessment for conditions including depression, anxiety, and suicidal ideation. For classification, Logistic Regression significantly outperforms Multinomial Naive Bayes, achieving an accuracy of 95% compared to 74%. To bridge the gap between detection and intervention, the system integrates a conversational module powered by LLaMA 2 (7B). Activated when severe risk is detected, this module provides context-aware interaction to support individuals and mitigate their vulnerability to both psychological crisis and digital exploitation. Deployed via Streamlit, this research serves as an assistive tool for professionals, highlighting the potential of combining machine learning with conversational AI to secure the human element against multifaceted digital and cognitive threats.

Social media virality significantly shapes public discourse during geopolitical conflicts, where emotionally charged and multimodal content can rapidly gain widespread attention. However, most prior approaches rely on retrospective engagement signals, limiting their usefulness for early prediction. Multimodal virality modeling in high-stakes Arabic discourse remains largely unexplored. We introduce NakbaVirality, a shared task on multimodal virality classification in conflict-related social media posts, organized as part of the Nakba-NLP workshop at LREC 2026. The dataset consists of 2,600 anonymized posts from X and Reddit collected after October 7, 2023, each including text, an associated image, and normalized engagement labels. Participants must classify posts into low, medium, or high virality categories using only textual and visual inputs. The task provides standardized splits, baseline systems, and evaluation using macro-F1 and accuracy. NakbaVirality establishes the first benchmark for multimodal virality prediction in Arabic high-stakes discourse and promotes research on contextual and multimodal modeling for early impact prediction. The shared task attracted 18 participants, who contributed a total of 4 official test phase submissions.

Small language models (SLMs) offer computationally efficient alternatives to large language models, yet their translation quality for low-resource languages (LRLs) remains severely limited. This work presents the first large-scale evaluation of SLMs across 200 languages, revealing systematic underperformance in LRLs and identifying key sources of linguistic disparity. We show that knowledge distillation from strong teacher models using predominantly monolingual LRL data substantially boosts SLM translation quality—often enabling 2B–3B models to match or surpass systems up to 70B parameters. Our study highlights three core findings: (1) a comprehensive benchmark exposing the limitations of SLMs on 200 languages; (2) evidence that LRL-focused distillation improves translation without inducing catastrophic forgetting, with full-parameter fine-tuning and decoder-only teachers outperforming LoRA and encoder–decoder approaches; and (3) consistent cross-lingual gains demonstrating the scalability and robustness of the method. These results establish an effective, low-cost pathway for improving LRL translation and provide practical guidance for deploying SLMs in truly low-resource settings.

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.

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.

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.

The task of converting natural language questions into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational databases. Many benchmarks, such as SPIDER and WikiSQL, have contributed to the development of new models and the evaluation of their performance. In addition, other datasets, like SEDE and BIRD, have introduced more challenges and complexities to better map real-world scenarios. However, these datasets primarily focus on high-resource languages such as English and Chinese. In this work, we introduce Dialect2SQL, the first large-scale, cross-domain text-to-SQL dataset in an Arabic dialect. It consists of 9,428 NLQ-SQL pairs across 69 databases in various domains. Along with SQL-related challenges such as long schemas, dirty values, and complex queries, our dataset also incorporates the complexities of the Moroccan dialect, which is known for its diverse source lan-guages, numerous borrowed words, and unique expressions. This demonstrates that our dataset will be a valuable contribution to both the text-to-SQL community and the development of resources for low-resource languages.

The generation of highly fluent text by Large Language Models (LLMs) poses a significant challenge to information integrity and academic research. In this paper, we introduce the Multi-Domain Detection of AI-Generated Text (M-DAIGT) shared task, which focuses on detecting AI-generated text across multiple domains, particularly in news articles and academic writing. M-DAIGT comprises two binary classification subtasks: News Article Detection (NAD) (Subtask 1) and Academic Writing Detection (AWD) (Subtask 2). To support this task, we developed and released a new large-scale benchmark dataset of 30,000 samples, balanced between human-written and AI-generated texts. The AI-generated content was produced using a variety of modern LLMs (e.g., GPT-4, Claude) and diverse prompting strategies. A total of 46 unique teams registered for the shared task, of which four teams submitted final results. All four teams participated in both Subtask 1 and Subtask 2. We describe the methods employed by these participating teams and briefly discuss future directions for M-DAIGT.

The hospitality industry in the Arab world increasingly relies on customer feedback to shape services, driving the need for advanced Arabic sentiment analysis tools. To address this challenge, the Sentiment Analysis on Arabic Dialects in the Hospitality Domain shared task focuses on Sentiment Detection in Arabic Dialects. This task leverages a multi-dialect, manually curated dataset derived from hotel reviews originally written in Modern Standard Arabic (MSA) and translated into Saudi and Moroccan (Darija) dialects. The dataset consists of 538 sentiment-balanced reviews spanning positive, neutral, and negative categories. Translations were validated by native speakers to ensure dialectal accuracy and sentiment preservation. This resource supports the development of dialect-aware NLP systems for real-world applications in customer experience analysis. More than 40 teams have registered for the shared task, with 12 submitting systems during the evaluation phase. The top-performing system achieved an F1 score of 0.81, demonstrating the feasibility and ongoing challenges of sentiment analysis across Arabic dialects.

Despite recent progress in large language models (LLMs), their performance on Arabic dialects remains underexplored, particularly in the context of sentiment analysis. This study presents a comparative evaluation of three LLMs, DeepSeek-R1, Qwen2.5, and LLaMA-3, on sentiment classification across Modern Standard Arabic (MSA), Saudi dialect and Darija. We construct a balanced sentiment dataset by translating and validating MSA hotel reviews into Saudi dialect and Darija. Using parameter-efficient fine-tuning (LoRA) and dialect-specific prompts, we assess each model under matched and mismatched prompting conditions. Evaluation results show that Qwen2.5 achieves the highest macro F1 score of 79% on Darija input using MSA prompts, while DeepSeek performs best when prompted in the input dialect, reaching 71% on Saudi dialect. LLaMA-3 exhibits stable performance across prompt variations, with 75% macro F1 on Darija input under MSA prompting. Dialect-aware prompting consistently improves classification accuracy, particularly for neutral and negative sentiment classes.

Arabic machine translation remains a fundamentally challenging task, primarily due to the lack of comprehensive annotated resources. This study evaluates the performance of Meta’s NLLB-200 model in translating Modern Standard Arabic (MSA) into three regional dialects: Saudi, Maghribi, and Egyptian Arabic using a manually curated dataset of hotel reviews. We applied a multi-criteria human annotation framework to assess translation correctness, dialect accuracy, and sentiment and aspect preservation. Our analysis reveals significant variation in translation quality across dialects. While sentiment and aspect preservation were generally high, dialect accuracy and overall translation fidelity were inconsistent. For Saudi Arabic, over 95% of translations required human correction, highlighting systemic issues. Maghribi outputs demonstrated better dialectal authenticity, while Egyptian translations achieved the highest reliability with the lowest correction rate and fewest multi-criteria failures. These results underscore the limitations of current multilingual models in handling informal Arabic varieties and highlight the importance of dialect-sensitive evaluation.

Large language models (LLMs) have shown remarkable progress in reasoning abilities and general natural language processing (NLP) tasks, yet their performance on Arabic data, characterized by rich morphology, diverse dialects, and complex script, remains underexplored. This paper presents a comprehensive benchmarking study of multiple reasoning-focused LLMs, with a special emphasis on the newly introduced DeepSeek models, across a suite of fifteen Arabic NLP tasks. We experiment with various strategies, including zero-shot, few-shot, and fine-tuning. This allows us to systematically evaluate performance on datasets covering a range of applications to examine their capacity for linguistic reasoning under different levels of complexity. Our experiments reveal several key findings. First, carefully selecting just three in-context examples delivers an average uplift of over 13 F1 points on classification tasks—boosting sentiment analysis from 35.3% to 87.5% and paraphrase detection from 56.1% to 87.0%. Second, reasoning-focused DeepSeek architectures outperform a strong GPT o4-mini baseline by an average of 12 F1 points on complex inference tasks in the zero-shot setting. Third, LoRA-based fine-tuning yields up to an additional 8 points in F1 and BLEU compared to equivalent increases in model scale. The code is available at https://anonymous.4open.science/r/AraReasoner41299

Recent advancements in large language models (LLMs) have significantly improved software development automation, including bug localization, code synthesis, program repair, and test generation. However, most prior work on program repair focuses on isolated elements, such as classes or functions, neglecting their interdependencies, which limits repair accuracy. We present SynFix, a RelationGraph-based approach that integrates LLMs with structural search and synchronization techniques for coordinated program repair across codebases. SynFix constructs a RelationGraph to capture relationships among classes, functions, variables, and their interactions (e.g., imports, inheritance, dependencies). Each RelationGraph node includes detailed code descriptions to help LLMs understand root causes and retrieve relevant contexts. By analyzing one-hop nodes in the RelationGraph, SynFixensures repairs account for dependent updates across components. Patch validation is conducted using regression tests from the SWE-bench benchmark suite. Evaluated on SWE-bench datasets, SynFix resolves 52.33% of issues in SWE-bench-lite (300 GitHub issues), 55.8% in SWE-bench-verified (500 issues), and 29.86% in SWE-bench-full (2,294 issues), outperforming baselines such as Swe-Agent, Agentless and AutoCodeRover. The codebase is available at https://anonymous.4open.science/r/AutoFix-EC86/.

We present an overview of the AraGenEval shared task, organized as part of the ArabicNLP 2025 conference. This task introduced the first benchmark suite for Arabic authorship analysis, featuring three subtasks: Authorship Style Transfer, Authorship Identification, and AI-Generated Text Detection. We curated high-quality datasets, including over 47,000 paragraphs from 21 authors and a balanced corpus of human- and AI-generated texts. The task attracted significant global participation, with 72 registered teams from 16 countries. The results highlight the effectiveness of transformer-based models, with top systems leveraging prompt engineering for style transfer, model ensembling for authorship identification, and a mix of multilingual and Arabic-specific models for AI text detection. This paper details the task design, datasets, participant systems, and key findings, establishing a foundation for future research in Arabic stylistics and trustworthy NLP.

The “Multilingual Corpus of World’s Constitutions” (MCWC) serves as a valuable resource for the NLP community, offering a comprehensive collection of constitutions from around the world. Its focus on data quality and breadth of coverage enables advanced research in constitutional analysis, machine translation, and cross-lingual legal studies. The MCWC prepares its data to ensure high quality and minimal noise, while also providing valuable mappings of constitutions to their respective countries and continents, facilitating comparative analysis. Notably, the corpus offers pairwise sentence alignments across languages, supporting machine translation experiments. We utilise a leading Machine Translation model, fine-tuned on the MCWC to achieve accurate and context-aware translations. Additionally, we introduce an independent Machine Translation model as a comparative baseline. Fine-tuning the model on the MCWC improves accuracy, highlighting the significance of such a legal corpus for NLP and Machine Translation. The MCWC’s rich multilingual content and rigorous data quality standards raise the bar for legal text analysis and inspire innovation in the NLP community, opening new avenues for studying constitutional texts and multilingual data analysis.

Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-intensive process that the research community is looking to automate. Existing automated methods rely on single input-output generative models and thus generally struggle to emulate the collaborative nature of code review. This work introduces CodeAgent, a novel multi-agent Large Language Model (LLM) system for code review automation. CodeAgent incorporates a supervisory agent, QA-Checker, to ensure that all the agents’ contributions address the initial review question. We evaluated CodeAgent on critical code review tasks: (1) detect inconsistencies between code changes and commit messages, (2) identify vulnerability introductions, (3) validate code style adherence, and (4) suggest code revisions. The results demonstrate CodeAgent’s effectiveness, contributing to a new state-of-the-art in code review automation. Our data and code are publicly available (https://github.com/Daniel4SE/codeagent).

The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (ii) Cross-dialect Translation and Intent Preservation. This shared task uses the updated ArBanking77 dataset, which includes about 39k parallel queries in MSA and four dialects. Each query is labeled with one or more of a common 77 intents in the banking domain. These resources aim to foster the development of robust financial Arabic NLP, particularly in the areas of machine translation and banking chat-bots.A total of 45 unique teams registered for this shared task, with 11 of them actively participated in the test phase. Specifically, 11 teams participated in Subtask 1, while only 1 team participated in Subtask 2. The winning team of Subtask 1 achieved F1 score of 0.8773, and the only team submitted in Subtask 2 achieved a 1.667 BLEU score.

Data anonymisation is often required to comply with regulations when transfering information across departments or entities. However, the risk is that this procedure can distort the data and jeopardise the models built on it. Intuitively, the process of training an NLP model on anonymised data may lower the performance of the resulting model when compared to a model trained on non-anonymised data. In this paper, we investigate the impact of de-identification on the performance of nine downstream NLP tasks. We focus on the anonymisation and pseudonymisation of personal names and compare six different anonymisation strategies for two state-of-the-art pre-trained models. Based on these experiments, we formulate recommendations on how the de-identification should be performed to guarantee accurate NLP models. Our results reveal that de-identification does have a negative impact on the performance of NLP models, but this impact is relatively low. We also find that using pseudonymisation techniques involving random names leads to better performance across most tasks.