3 citations · 8 across the 12 of their papers we have counts for
12 papers
SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
Usman Naseem, Robert Geislinger, Juan Ren +31
We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labe…
Scaling Truth: The Confidence Paradox in AI Fact-Checking
Ihsan A. Qazi, Zohaib Khan, Abdullah Ghani +7
The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet th…
TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses
Muhammad Taha Cheema, Abeer Aamir, Khawaja Gul Muhammad +3
Large Language Models (LLMs) process millions of queries daily, making efficient response caching a compelling optimization for reducing cost and latency. However, preserving relev…
Semantic Caching for Improving Web Affordability
Hafsa Akbar, Danish Athar, Muhammad Ayain Fida Rana +4
The rapid growth of web content has led to increasingly large webpages, posing significant challenges for Internet affordability, especially in developing countries where data cost…
Language Model-Driven Data Pruning Enables Efficient Active Learning
Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
Active learning (AL) optimizes data labeling efficiency by selecting the most informative instances for annotation. A key component in this procedure is an acquisition function tha…
Rethinking Image Compression on the Web with Generative AI
Shayan Ali Hassan, Danish Humair, Ihsan Ayyub Qazi +1
The rapid growth of the Internet, driven by social media, web browsing, and video streaming, has made images central to the Web experience, resulting in significant data transfer a…
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation
Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
Active learning (AL) techniques reduce labeling costs for training neural machine translation (NMT) models by selecting smaller representative subsets from unlabeled data for annot…
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition
Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
Self-supervised speech recognition models require considerable labeled training data for learning high-fidelity representations for Automatic Speech Recognition (ASR) which is comp…
FedPrune: Towards Inclusive Federated Learning
Muhammad Tahir Munir, Muhammad Mustansar Saeed, Mahad Ali +2
Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrade…
Countering Misinformation on Social Media Through Educational Interventions: Evidence from a Randomized Experiment in Pakistan
Ayesha Ali, Ihsan Ayyub Qazi
Fake news is a growing problem in developing countries with potentially far-reaching consequences. We conduct a randomized experiment in urban Pakistan to evaluate the effectivenes…
Investigating Misinformation Dissemination on Social Media in Pakistan
Danyal Haroon, Hammad Arif, Ahmed Abdullah Tariq +3
Fake news and misinformation are one of the most significant challenges brought about by advances in communication technologies. We chose to research the spread of fake news in Pak…
Reducing Tail Latency via Safe and Simple Duplication
Hafiz Mohsin Bashir, Abdullah Bin Faisal, Muhammad Asim Jamshed +4
Duplication can be a powerful strategy for overcoming stragglers in cloud services, but is often used conservatively because of the risk of overloading the system. We present dupli…