6 papers
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs
Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy +41
As large language models (LLMs) become increasingly integrated into daily life, ensuring their cultural sensitivity and inclusivity is paramount. We introduce our dataset, a year-l…
Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models
Hanin Atwany, Abdul Waheed, Rita Singh +2
Speech foundation models trained at a massive scale, both in terms of model and data size, result in robust systems capable of performing multiple speech tasks, including automatic…
On the Robust Approximation of ASR Metrics
Abdul Waheed, Hanin Atwany, Rita Singh +1
Recent advances in speech foundation models are largely driven by scaling both model size and data, enabling them to perform a wide range of tasks, including speech recognition. Tr…
JEEM: Vision-Language Understanding in Four Arabic Dialects
Karima Kadaoui, Hanin Atwany, Hamdan Al-Ali +7
We introduce JEEM, a benchmark designed to evaluate Vision-Language Models (VLMs) on visual understanding across four Arabic-speaking countries: Jordan, The Emirates, Egypt, and Mo…
What Do Speech Foundation Models Not Learn About Speech?
Abdul Waheed, Hanin Atwany, Bhiksha Raj +1
Understanding how speech foundation models capture non-verbal cues is crucial for improving their interpretability and adaptability across diverse tasks. In our work, we analyze se…