2 citations · 3 across the 10 of their papers we have counts for
9 papers · 1 filter
Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs
Abdellah El Mekki, Samar M. Magdy, Houdaifa Atou +44
Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic (MSA). Despite this, machine translation (MT) sy…
Transformer Tafsir at QIAS 2025 Shared Task: Hybrid Retrieval-Augmented Generation for Islamic Knowledge Question Answering
Muhammad Abu Ahmad, Mohamad Ballout, Raia Abu Ahmad +1
This paper presents our submission to the QIAS 2025 shared task on Islamic knowledge understanding and reasoning. We developed a hybrid retrieval-augmented generation (RAG) system…
Pixels to Principles: Probing Intuitive Physics Understanding in Multimodal Language Models
Mohamad Ballout, Serwan Jassim, Elia Bruni
This paper presents a systematic evaluation of state-of-the-art multimodal large language models (MLLMs) on intuitive physics tasks using the GRASP and IntPhys 2 datasets. We asses…
iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs
Julius Mayer, Mohamad Ballout, Serwan Jassim +2
Vision-Language Models (VLMs) are known to struggle with spatial reasoning and visual alignment. To help overcome these limitations, we introduce iVISPAR, an interactive multimodal…
Enhancing SLM via ChatGPT and Dataset Augmentation
Tom Pieper, Mohamad Ballout, Ulf Krumnack +2
This paper explores the enhancement of small language models through strategic dataset augmentation via ChatGPT-3.5-Turbo, in the domain of Natural Language Inference (NLI). By emp…
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Enhancing small language models for real-life application deployment is a significant challenge facing the research community. Due to the difficulties and costs of using large lang…
Show Me How It's Done: The Role of Explanations in Fine-Tuning Language Models
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Our research demonstrates the significant benefits of using fine-tuning with explanations to enhance the performance of language models. Unlike prompting, which maintains the model…
Investigating Pre-trained Language Models on Cross-Domain Datasets, a Step Closer to General AI
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Pre-trained language models have recently emerged as a powerful tool for fine-tuning a variety of language tasks. Ideally, when models are pre-trained on large amount of data, they…
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Investigating deep learning language models has always been a significant research area due to the ``black box" nature of most advanced models. With the recent advancements in pre-…