1 citations · 1 across the 15 of their papers we have counts for
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Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs
Muhammed Saeed, Muhammad Abdul-mageed, Shady Shehata
Large language models (LLMs) are widely deployed for open-ended communication, yet most bias evaluations still rely on English, classification-style tasks. We introduce \corpusname…
NurseLLM: The First Specialized Language Model for Nursing
Md Tawkat Islam Khondaker, Julia Harrington, Shady Shehata
Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains larg…
ASR Under Noise: Exploring Robustness for Sundanese and Javanese
Salsabila Zahirah Pranida, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi +1
We investigate the robustness of Whisper-based automatic speech recognition (ASR) models for two major Indonesian regional languages: Javanese and Sundanese. While recent work has…
ArFake: A Robust Framework for Multi-Dialect Arabic Speech Spoofing Detection Benchmark
Mohamed Elsetohy, Alhassan Ehab, Ali Mekky +2
With the rise of generative text-to-speech models, distinguishing between real and synthetic speech has become challenging, especially for Arabic that have received limited researc…
ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Ali Abouzeid, Bilal Elbouardi, Mohamed Maged +1
Speech emotion recognition is vital for human-computer interaction, particularly for low-resource languages like Arabic, which face challenges due to limited data and research. We…
Bridging ASR and LLMs for Dysarthric Speech Recognition: Benchmarking Self-Supervised and Generative Approaches
Ahmed Aboeitta, Ahmed Sharshar, Youssef Nafea +1
Speech Recognition (ASR) due to phoneme distortions and high variability. While self-supervised ASR models like Wav2Vec, HuBERT, and Whisper have shown promise, their effectiveness…
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
Muhammed Saeed, Shaina Raza, Ashmal Vayani +3
Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does gramma…
BALSAM: A Platform for Benchmarking Arabic Large Language Models
Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed +40
The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data s…
Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset
Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki +34
Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a…
NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities
Abdellah El Mekki, Houdaifa Atou, Omer Nacar +2
Enhancing the linguistic capabilities of Large Language Models (LLMs) to include low-resource languages is a critical research area. Current research directions predominantly rely…
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…
Jawaher: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking
Samar M. Magdy, Sang Yun Kwon, Fakhraddin Alwajih +3
Recent advancements in instruction fine-tuning, alignment methods such as reinforcement learning from human feedback (RLHF), and optimization techniques like direct preference opti…