281 citations · 652 across the 50 of their papers we have counts for
56 papers
ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation
Samir Abdaljalil, Hunzalah Hassan Bhatti, Ahlam Bashiti +11
We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question ans…
Said Aloud, Read Different: Cross-Modal Instability in Multimodal Models
Basel Mousi, Fahim Dalvi, Shammur Chowdhury +2
Multimodal foundation models are increasingly used in speech-first assistants that must interpret spoken queries and produce visually grounded decisions. Yet it remains unclear whe…
Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs
Kareem Elozeiri, Mervat Abassy, Omar Kallas +4
Dialectal data are scarce relative to Modern Standard Arabic (MSA), causing Arabic LLMs to overproduce MSA and struggle with dialectally accurate generation. This raises a fundamen…
Fanar 2.0: Arabic Generative AI Stack
FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34
We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…
Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models
Basel Mousi, Fahim Dalvi, Shammur Chowdhury +2
Vision-language models (VLMs) can achieve high accuracy while still accepting culturally plausible but visually incorrect interpretations. Existing hallucination benchmarks rarely…
OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA
Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan +8
Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries require cultural and visual information,…
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…
An Exploration of Knowledge Editing for Arabic
Basel Mousi, Nadir Durrani, Fahim Dalvi
While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the fir…
The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology
Shahad Al-Khalifa, Nadir Durrani, Hend Al-Khalifa +1
The emergence of ChatGPT marked a transformative milestone for Artificial Intelligence (AI), showcasing the remarkable potential of Large Language Models (LLMs) to generate human-l…
From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models
Asım Ersoy, Basel Mousi, Shammur Chowdhury +3
The emergence of large language models (LLMs) has demonstrated that systems trained solely on text can acquire extensive world knowledge, develop reasoning capabilities, and intern…
Editing Across Languages: A Survey of Multilingual Knowledge Editing
Nadir Durrani, Basel Mousi, Fahim Dalvi
While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multili…
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…
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs
Basel Mousi, Nadir Durrani, Fatema Ahmad +6
Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introduci…
Exploring Alignment in Shared Cross-lingual Spaces
Basel Mousi, Nadir Durrani, Fahim Dalvi +2
Despite their remarkable ability to capture linguistic nuances across diverse languages, questions persist regarding the degree of alignment between languages in multilingual embed…
Latent Concept-based Explanation of NLP Models
Xuemin Yu, Fahim Dalvi, Nadir Durrani +2
Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts aimed at expla…
Scaling up Discovery of Latent Concepts in Deep NLP Models
Majd Hawasly, Fahim Dalvi, Nadir Durrani
Despite the revolution caused by deep NLP models, they remain black boxes, necessitating research to understand their decision-making processes. A recent work by Dalvi et al. (2022…
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…
NeuroX Library for Neuron Analysis of Deep NLP Models
Fahim Dalvi, Hassan Sajjad, Nadir Durrani
Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding o…
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…
Can LLMs facilitate interpretation of pre-trained language models?
Basel Mousi, Nadir Durrani, Fahim Dalvi
Work done to uncover the knowledge encoded within pre-trained language models rely on annotated corpora or human-in-the-loop methods. However, these approaches are limited in terms…
NxPlain: Web-based Tool for Discovery of Latent Concepts
Fahim Dalvi, Nadir Durrani, Hassan Sajjad +3
The proliferation of deep neural networks in various domains has seen an increased need for the interpretability of these models, especially in scenarios where fairness and trust a…
Evaluating Neuron Interpretation Methods of NLP Models
Yimin Fan, Fahim Dalvi, Nadir Durrani +1
Neuron Interpretation has gained traction in the field of interpretability, and have provided fine-grained insights into what a model learns and how language knowledge is distribut…
ConceptX: A Framework for Latent Concept Analysis
Firoj Alam, Fahim Dalvi, Nadir Durrani +3
The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework fo…
On the Transformation of Latent Space in Fine-Tuned NLP Models
Nadir Durrani, Hassan Sajjad, Fahim Dalvi +1
We study the evolution of latent space in fine-tuned NLP models. Different from the commonly used probing-framework, we opt for an unsupervised method to analyze representations. M…