2 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
Vision Language Models are Confused Tourists
Patrick Amadeus Irawan, Ikhlasul Akmal Hanif, Muhammad Dehan Al Kautsar +3
Although the cultural dimension has been one of the key aspects in evaluating Vision-Language Models (VLMs), their ability to remain stable across diverse cultural inputs remains l…
SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages
Muhammad Dehan Al Kautsar, Aswin Candra, Muhammad Alif Al Hakim +6
Although numerous datasets have been developed to support dialogue systems, most existing chit-chat datasets overlook the cultural nuances inherent in natural human conversations.…
Role-Aware Language Models for Secure and Contextualized Access Control in Organizations
Saeed Almheiri, Yerulan Kongrat, Adrian Santosh +4
As large language models (LLMs) are increasingly deployed in enterprise settings, controlling model behavior based on user roles becomes an essential requirement. Existing safety m…
IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages
Muhammad Falensi Azmi, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono +1
Although region-specific large language models (LLMs) are increasingly developed, their safety remains underexplored, particularly in culturally diverse settings like Indonesia, wh…
Evaluating Vision-Language and Large Language Models for Automated Student Assessment in Indonesian Classrooms
Nurul Aisyah, Muhammad Dehan Al Kautsar, Arif Hidayat +2
Despite rapid progress in vision-language and large language models (VLMs and LLMs), their effectiveness for AI-driven educational assessment in real-world, underrepresented classr…
Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation
Naila Shafirni Hidayat, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono +1
The performance of large language models (LLMs) continues to improve, as reflected in rising scores on standard benchmarks. However, the lack of transparency around training data r…