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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★ 2 cited
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…