The paper introduces UrduFactBench and UrduFactQA, two hand‑annotated benchmarks for claim verification and factual consistency evaluation in Urdu, created through a multi‑stage annotation process with native speakers. It also presents UrduFactCheck, a modular fact‑checking framework that uses both monolingual and translation‑based evidence retrieval to address the scarcity of high‑quality Urdu evidence. Experiments on twelve LLMs show that translation‑augmented pipelines outperform monolingual ones, highlighting ongoing challenges for open‑source models in Urdu.
By Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan, Numaan Naeem, Muhammad Ahsan Riaz Khan, Arham Riaz, Muhammad Arslan Manzoor, Yuxia Wang, Preslav Nakov
The paper introduces a new benchmark for Urdu‑to‑English idiomatic translation, featuring 4,000 manually verified sentence pairs in both native Perso‑Arabic script and Romanized Urdu. It evaluates multiple tasks—translation, paraphrasing, idiom span detection, and back‑translation—using various prompting strategies, and finds that state‑of‑the‑art large language models outperform traditional neural machine translation systems, especially in preserving figurative meaning. The study also highlights challenges posed by the lack of standardized orthography in Romanized Urdu, which affects consistency and idiom span detection.
By Muhammad Farmal Khan, Mousumi Akter
The paper investigates how multilingual large language models perform when generating stories in Urdu, a low‑resource language. The authors created a corpus of 93 Urdu stories produced by GPT‑5.1, Qwen‑3‑Max, and DeepSeek‑3.1, and manually annotated errors across a nine‑label taxonomy covering linguistic, semantic, and cultural aspects. Findings reveal frequent grammatical and semantic mistakes, lack of coherence, unnatural repetition, and pervasive cultural shallowness, with few‑shot prompting failing to resolve many of these issues.
By Farah Adeeba, Abdul Rafae Khan, Rajesh Bhatt, Hassan Sajjad
The paper "Ghaib in Translation" investigates how large language models (LLMs) handle Urdu, a widely spoken language that is largely absent from safety evaluations. Five prominent LLMs—GPT‑4o, Claude Sonnet 4.5, Gemini 2.5 Flash, Qwen‑2.5, and Llama‑3.1—were tested on six datasets covering Nastaliq Urdu, Roman Urdu, English, and code‑switched Urdu‑English. The study found significant label instability between original‑script and English‑translation classifications, with missed‑in‑Urdu rates ranging from 2.4% to 9.9% (median 4.3%). A review of 205 papers across nine ALW/WOAH editions revealed no dedicated Urdu research, underscoring the language’s neglect in current safety research.
By Fawzia Zehra (Fuzzy), Kara-Isitt, Sonal Khosla, Stephen Swift
arXiv:2608.21408v1 Announce Type: new
Abstract: Due to the widespread accessibility of the internet and social media, toxic and hateful con-tent has grown exponentially, causing significant distress...
By Toneema Zubair
arXiv:2608.30065v1 Announce Type: cross
Abstract: Large language models (LLMs) demonstrate strong Helpfulness, Harmlessness, and Honesty (3H) alignment in English-centric settings, but these gains tr...
By Abdullah Hashmat, Usman Naseem, Agha Ali Raza
The paper tackles syntactic parsing for Urdu, a morphologically rich language, and reports state‑of‑the‑art results for both constituency and dependency parsing. It introduces four key contributions: converting the CLE‑UTB phrase structure treebank into a dependency treebank with language‑specific mapping rules, a novel sequence labeling scheme that unifies the parsing task, training contextualized word representations on a 220‑million‑token Urdu corpus, and a parsing framework that employs both single‑task and multi‑task learning. Experiments show that the multi‑task setup boosts performance, achieving an F1 score of 91.39 for constituency parsing and a labeled attachment score of 85.69 for dependency parsing, improving over previous results.
By Toqeer Ehsan, Miriam Butt, Sarmad Hussain, Hassan Alhuzali, Ali Al-Laith
Large language models (LLMs) demonstrate strong Helpfulness, Harmlessness, and Honesty (3H) alignment in English-centric settings, but these gains transfer poorly to low-resource languages due to cult...
Multilingual Language Models like mBERT are widely used for low-resource NLP, yet their adaptation to morphologically inconsistent languages such as Roman Urdu remains underexplored. Roman Urdu spelling variation causes severe sub-word fragmentation, averaging 1.
The study examines Urdu light verbs, which add schematic event meaning while staying lexically linked to their main verbs. Using contextual embeddings from UrduBERT, DunbaaBERT, and multilingual BERT on 1,126 sentences, the authors find significant representational separation between main and light uses across all verb–model pairs, yet main and light uses of the same lemma remain closer than mismatched pairs. In a seven‑way prediction task limited to light uses, UrduBERT achieves 0.866 accuracy and 0.852 macro‑F1, and maintains 0.782 accuracy when tested on unseen preceding forms, demonstrating generalization beyond local verb combinations.
By Farah Adeeba, Miriam Butt
arXiv:2305.12474v4 Announce Type: replace
Abstract: Large Language Models(LLMs) have demonstrated remarkable performance across various natural language processing tasks; however, how to comprehensiv...
By Xiaotian Zhang, Chunyang Li, Yi Zong, Zhengyu Ying, Liang He, Xipeng Qiu, Tianxiang Sun, Peng Li, Shiqiao Meng, Yanjun Zheng, Jun Zhan, Zhangyue Yin, Xiannian Hu, Guofeng Quan, Qixiang Wang
The paper evaluates large language models for hate‑speech detection in Roman Urdu, a low‑resource language with informal spelling variations. Using the Parameter‑Efficient Fine‑Tuning technique Low‑Rank Adaptation (LoRA), the authors fine‑tune models such as Mistral, LLaMA, Falcon, and multilingual BERT on the 72,000‑comment PURUTT dataset. While zero‑shot performance yields an F1 of 0.56, fine‑tuning a small fraction of parameters boosts F1 scores above 0.93, demonstrating that PEFT offers both high accuracy and computational efficiency for low‑resource language tasks.
By Toneema Zubair, Muhammad Junaid Asif, Faisal Kamiran, Hafiz Hassan Saeed, Rana Fayyaz Ahmad