The paper introduces Mawqif-XT, a new Arabic benchmark dataset comprising 996 manually annotated tweets from three public targets: Women Driving, E-Cars, and Trimester System. Each tweet is labeled for stance, sentiment, and sarcasm following the Mawqif annotation scheme, and the dataset is intended as a held‑out evaluation set to test cross‑target generalization. Baseline results are provided using Arabic and multilingual transformer models as well as zero‑shot large language models, enabling reproducible evaluation alongside the original Mawqif dataset.
By Rasha Albalawi, Nuha Albadi, Hamzah Luqman, Maram Kurdi, Saad Ezzini, Asma Yamani, Ahmed Ashraf
arXiv:2608. 03810v1 Announce Type: cross Abstract: Large language models routinely describe socially salient targets, including political figures, countries, religions, organizations, historical events, and social groups, encoding affective framing alongside factual content: a target may appear favorable or threatening, calm or conflictual, powerful or vulnerable.
By Andrei Chetvergov, Alexander Evseev, Timofei Sivoraksha, Stepan Ukolov, Mikhail Solovev, Danil Sazanakov, Sergey Bolovtsov
The paper introduces CLASP‑Ar, a cloze‑style prompting method for Arabic stance detection that replaces complex multitask learning and ensembles with a single masked language modeling prompt. By combining the target, predicted sentiment, and text into one prompt and constraining the [MASK] prediction to a verbalizer‑defined label set, the approach aims to simplify the task while maintaining performance.
By Bhuvanesh Verma, Ali Abusaleh, Alexander Mehler
MABPD (Multi‑Agent Bias Probing & Detection) is a training‑free pipeline that uses three specialized large language model agents to analyze news articles from complementary perspectives and resolve disagreements via a Structured Argument Debate (SAD) protocol. SAD imposes an asymmetric burden of proof—biased claims lacking grounded textual evidence receive zero weight—along with role‑weighted voting and post‑consensus verification, replacing task‑specific supervised decision boundaries. Ablation studies show that the debate module alone accounts for up to a 10.6‑point F1 gain, and on the BABE benchmark MABPD attains 83.4% macro F1, within 0.7 percentage points of the supervised state‑of‑the‑art, while achieving 75.0% zero‑shot accuracy on the SemEval 2019 HyperPartisan corpus.
By Garvit Joshi (Graphic Era University, Dehradun, India), Stavya Dhyani (Graphic Era University, Dehradun, India), Jasmine (Graphic Era University, Dehradun, India), Arun Chauhan (Graphic Era University, Dehradun, India)
HalluScoring 2026 is a shared task that evaluates hallucination detection and factual verification in Arabic question answering, focusing on generalization to unseen questions and LLMs. It comprises two main tasks with four subtasks: binary hallucination detection (Subtasks 1.1 and 1.2) and answer verification against six candidates in Islamic and general knowledge domains (Subtasks 2.1 and 2.2). Thirteen teams participated, with the top system achieving AUC‑ROC scores of 0.772 and 0.767 for detection, and 0.882 and 0.857 for verification.
By Aisha Alansari, Abdessalam Bouchekif, Ahmed Hasanaath, Salah Eddine Bekhouche, Malak Alkhorasani, Mohammed-En-Nadhir Zighem, Saad Ezzini, Hichem Telli, Hend Al-Khalifa, Muhammad Abdul-Mageed, Hadid Abdenour, Hamzah Luqman
arXiv:2607. 04690v1 Announce Type: cross Abstract: We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026.
By Md. Shakhoyat Rahman Shujon, MD Jahid Hasan Jim, Md. Milon Islam, Md Rezwanul Haque, Fakhri Karray
The paper introduces GPTBIAS, a framework that uses powerful large language models like GPT‑4 to evaluate bias in other LLMs. It employs specially crafted prompts called Bias Attack Instructions to probe for bias and outputs a bias score along with detailed information such as bias types, affected demographics, keywords, reasons, and improvement suggestions. Extensive experiments demonstrate the framework’s effectiveness and usability.
By Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, Mykola Pechenizkiy
arXiv:2609.27811v1 Announce Type: cross
Abstract: Online platforms have become arenas for the public contestation of climate change, shaping how scientific knowledge, denial, and uncertainty are expr...
By Daniel Morais, Diego H. M. Magalhaes, Gabriel H. Silva, Andrea Failla, Valeria de C. Santos, Helen C. S. C. Lima, Carlos H. G. Ferreira
arXiv:2509.22367v3 Announce Type: replace
Abstract: Large language models (LLMs) reflect politically-slanted opinions in their generated text. Even though it is widely assumed that model behavior ste...
By Tanise Ceron, Dmitry Nikolaev, Dominik Stammbach, Debora Nozza
arXiv:2608.21385v1 Announce Type: cross
Abstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose b...
By Michail Zafeiropoulos, Despoina Antonakaki, Sotiris Ioannidis
arXiv:2607. 24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling.
By Heyan Chai, Xin Li, Wenjie Wang, Jianyang Qin, Chaoyang Li, Lu Wang, Hao Chen, Qing Liao
The paper reports on the N"urnberg NLP team’s system for the GermEval 2026 shared task on harmful content detection in German social media. The authors tackle severe class imbalance by building a nine‑voter ensemble that varies along three orthogonal axes—LLM choice, training method, and class scope—to achieve error independence. Their system attains macro‑F1 scores of 89.56 (C2A), 71.63 (DBO), 54.84 (VIO), and 83.02 (DEF) on the hidden test set, winning all four subtasks.
By Philipp Steigerwald, Eric Rudolph, Jens Albrecht