We introduce VLURes, a multilingual benchmark for evaluating Vision-Language Models (VLMs) under long-text grounding: selecting and reasoning over the image-relevant subset of article-length text that contains distractors and ungrounded claims. VLURes contains 4,000 web-curated image + long-text pairs across English (En), Japanese (Ja), Swahili (Sw), and Urdu (Ur) and 10 topical categories, and defines eight tasks spanning image-only perception (OR, SU, RU, SS, IC) and image+text grounding (ITM, Unrelatedness, VQA). To construct web-realistic pairs, we apply language-adapted CLIP alignment to select representative images and filter weakly grounded pages. Across 10 proprietary and open VLMs evaluated under zero-shot and one-shot prompting, with and without rationales, the best model (GPT-4o) reaches 90.8% overall accuracy but remains 6.7 points below human performance (97.5%) on Object Recognition, and cross-lingual sensitivity persists, while open models are substantially weaker and often lack reliable multilingual VL support. VLURes provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings.

The rise of large language models (LLMs) generating human-like text has raised concerns about misuse, especially in low-resource languages like Urdu. To address this gap, we introduce the HLU dataset, which consists of three datasets: Document, Paragraph, and Sentence level. The document-level dataset contains 1,014 instances of human-written and LLM-generated articles across 13 domains, while the paragraph and sentence-level datasets each contain 667 instances. We conducted both human and automatic evaluations. In the human evaluation, the average accuracy at the document level was 35%, while at the paragraph and sentence levels, accuracies were 75.68% and 88.45%, respectively. For automatic evaluation, we finetuned the XLMRoBERTa model for both monolingual and multilingual settings achieving consistent results in both. Additionally, we assessed the performance of GPT4 and Claude3Opus using zero-shot prompting. Our experiments and evaluations indicate that distinguishing between human and machine-generated text is challenging for both humans and LLMs, marking a significant step in addressing this issue in Urdu.