arXiv:2610.23093v1 Announce Type: new
Abstract: LLM agents increasingly use retrieval tools across languages, yet their safety evaluation remains English-centric. It remains unclear whether language...
By Aditya Nawal, Manit Baser, Mohan Gurusamy
IndicSafeEval is a new evaluation framework that tests the safety robustness of large language models against persuasion-based jailbreak attacks in Indian languages. The benchmark includes 7,200 adversarial prompts covering ten safety-critical content categories, six persuasive strategies, and four languages (Hindi, Bengali, Marathi, Punjabi). Experiments show that model safety varies significantly across languages, prompt styles, and risk categories, revealing that current English-centric safety evaluations miss important multilingual vulnerabilities.
By Saikat Mondal, Mamta, Deeksha Varshney, Oana Cocarascu, Asif Ekbal
IndicSafeEval is a new evaluation framework that tests the safety robustness of large language models against persuasion-based jailbreak attacks in Indian languages. The benchmark covers ten safety-critical content categories, six persuasive strategies, and four languages—Hindi, Bengali, Marathi, and Punjabi—producing 7,200 adversarial prompts. Experiments show that model safety varies significantly across languages, prompt styles, and risk categories, highlighting gaps in current English-centric safety assessments.
arXiv:2609.22119v1 Announce Type: cross
Abstract: Evaluation awareness poses an unprecedented threat to model evaluation, but the mechanisms by which models detect it remain unknown. This study focus...
By Navraj Singh, Maheep Chaudhary
The study investigates how different prompting strategies affect large language model (LLM) judges across multiple languages and tasks. Using a benchmark of 9 prompting strategies, 4 open-weight judges, 27 languages, and 4 tasks, the authors collected over 1 million pointwise scores validated against human judgments. Results show that prompting effectiveness is highly dependent on the judge model, can reverse direction across judges, and becomes less stable in low‑resource languages, indicating that prompt choice must be tailored to the specific evaluation setting.
By Asal Mehrabi, Mohammad Aliannejadi, Clemencia Siro
arXiv:2610.14443v1 Announce Type: cross
Abstract: Model cards, system cards, and safety reports increasingly document LLM safety behaviors, yet safety evaluations typically start from predefined benc...
By Kausar Moshood, Jace Bolante, Roshan Aditya Murali, Manish Motwani