6 papers · 1 filter
M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks
Blessed Guda, Carlee Joe-Wong
The rise of 5G/6G network technologies promises to enable applications like autonomous vehicles and virtual reality, resulting in a significant increase in connected devices and ne…
Pose-Based Sign Language Spotting via an End-to-End Encoder Architecture
Samuel Ebimobowei Johnny, Blessed Guda, Emmanuel Enejo Aaron +1
Automatic Sign Language Recognition (ASLR) has emerged as a vital field for bridging the gap between deaf and hearing communities. However, the problem of sign-to-sign retrieval or…
Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach
Blessed Guda, Lawrence Francis, Gabrial Zencha Ashungafac +2
Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ t…
AutoSign: Direct Pose-to-Text Translation for Continuous Sign Language Recognition
Samuel Ebimobowei Johnny, Blessed Guda, Andrew Blayama Stephen +1
Continuously recognizing sign gestures and converting them to glosses plays a key role in bridging the gap between the hearing and hearing-impaired communities. This involves recog…
AmaSQuAD: A Benchmark for Amharic Extractive Question Answering
Nebiyou Daniel Hailemariam, Blessed Guda, Tsegazeab Tefferi
This research presents a novel framework for translating extractive question-answering datasets into low-resource languages, as demonstrated by the creation of the AmaSQuAD dataset…
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
Blessed Guda, Gabrial Zencha Ashungafac, Lawrence Francis +1
Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems. These models do remarkably well in addressing intricate in…