3 papers
cs.CL2025
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…
cs.CL2025
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…
eess.SY2024
Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
Lawrence Francis, Blessed Guda, Ahmed Biyabani
In reinforcement learning-based (RL-based) traffic signal control (TSC), decisions on the signal timing are made based on the available information on vehicles at a road intersecti…