14 citations · 46 across the 18 of their papers we have counts for
20 papers
Reinforcement Learning with Comparative Evidence for Social Intelligence
Keane Ong, Yuriel Ryan, Sabri Boughorbel +9
Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive…
Latent Mechanisms of Language Control in Multilingual Language Models
Ryo Mitsuhashi, Sabri Boughorbel, Majd Hawasly
Multilingual large language models can exhibit unintended code-switching -- unnecessarily alternating between languages during generation. We present a comparative study of three m…
Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy
Asif Azad, Mohammad Sadat Hossain, MD Sadik Hossain Shanto +6
Children with Autism Spectrum Disorder in Arabic-speaking countries face compounded barriers to effective speech and language therapy: a shortage of qualified specialists, limited…
Design and Evaluation of a Culturally Adapted Multimodal Virtual Agent for PTSD Screening
Cengiz Ozel, Waleed Nadeem, Samuel Potter +8
Post-traumatic stress disorder (PTSD) is highly prevalent yet chronically underreported among combat-exposed military personnel. This paper presents Molhim, a culturally adapted mu…
Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment
Asif Azad, MD Sadik Hossain Shanto, Mohammad Sadat Hossain +6
Automated phoneme-level pronunciation assessment is vital for scalable speech therapy and language learning, yet validated tools for Arabic remain scarce. We present Harf-Speech, a…
There Is More to Refusal in Large Language Models than a Single Direction
Faaiz Joad, Majd Hawasly, Sabri Boughorbel +2
Prior work argues that refusal in large language models is mediated by a single direction, enabling steering and abliteration. We show that this account is incomplete: across diver…
OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization
Keane Ong, Sabri Boughorbel, Luwei Xiao +9
Socially intelligent AI systems must reason across diverse human behavioral tasks and generalize to new social contexts. However, behavioral data is inherently heterogeneous, compr…
Beyond the Leaderboard: Understanding Performance Disparities in Large Language Models via Model Diffing
Sabri Boughorbel, Fahim Dalvi, Nadir Durrani +1
As fine-tuning becomes the dominant paradigm for improving large language models (LLMs), understanding what changes during this process is increasingly important. Traditional bench…
Fanar: An Arabic-Centric Multimodal Generative AI Platform
Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39
We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis
Sabri Boughorbel, MD Rizwan Parvez, Majd Hawasly
Training LLMs for low-resource languages usually utilizes data augmentation from English using machine translation (MT). This, however, brings a number of challenges to LLM trainin…
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling
Sharmita Dey, Benjamin Paassen, Sarath Ravindran Nair +2
Lower limb amputations and neuromuscular impairments severely restrict mobility, necessitating advancements beyond conventional prosthetics. While motorized bionic limbs show promi…
Semantic Ranking for Automated Adversarial Technique Annotation in Security Text
Udesh Kumarasinghe, Ahmed Lekssays, Husrev Taha Sencar +3
We introduce a new method for extracting structured threat behaviors from threat intelligence text. Our method is based on a multi-stage ranking architecture that allows jointly op…
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic
Sabri Boughorbel, Majd Hawasly
While significant progress has been made in benchmarking Large Language Models (LLMs) across various tasks, there is a lack of comprehensive evaluation of their abilities in respon…
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
Fahim Dalvi, Maram Hasanain, Sabri Boughorbel +10
The recent development and success of Large Language Models (LLMs) necessitate an evaluation of their performance across diverse NLP tasks in different languages. Although several…
LAraBench: Benchmarking Arabic AI with Large Language Models
Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury +13
Recent advancements in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. Despite this progress, these models lack specific b…
Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department
Sabri Boughorbel, Fethi Jarray, Abdulaziz Al Homaid +2
Language modeling have shown impressive progress in generating compelling text with good accuracy and high semantic coherence. An interesting research direction is to augment these…
Learning a Shared Model for Motorized Prosthetic Joints to Predict Ankle-Joint Motion
Sharmita Dey, Sabri Boughorbel, Arndt F. Schilling
Control strategies for active prostheses or orthoses use sensor inputs to recognize the user's locomotive intention and generate corresponding control commands for producing the de…
Fairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show
Sabri Boughorbel, Fethi Jarray, Abdou Kadri
Patient no-shows is a major burden for health centers leading to loss of revenue, increased waiting time and deteriorated health outcome. Developing machine learning (ML) models fo…
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
Sabri Boughorbel, Fethi Jarray, Neethu Venugopal +3
Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diver…
Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels
Sabri Boughorbel, Fethi Jarray, Neethu Venugopal +1
In this paper we are interested in the prediction of preterm birth based on diagnosis codes from longitudinal EHR. We formulate the prediction problem as a supervised classificatio…