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…
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…
cs.CL2024
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…