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 tasks, where their choices are influenced by factors like answer position or option symbols rather than the content. This bias undermines the reliability of MCQ as an evaluation framework. Most existing selection bias metrics require answer labels and measure divergences between prediction and answer distributions, but do not fully capture the consistency of a model’s predictions across different orderings of answer choices. Existing selection bias mitigation strategies have notable limitations: majority voting, though effective, is computationally prohibitive; calibration-based methods require validation sets and often fail to generalise across datasets. To address these gaps, we propose three key contributions: (1) a new unsupervised label-free Permutation Bias Metric (PBM) that directly quantifies inconsistencies in model predictions across answer permutations, providing a more precise measure of selection bias, (2) an efficient majority voting approach called Batch Question-Context KV caching (BaQCKV), to significantly reduce computational costs while preserving bias mitigation effectiveness, and (3) an unsupervised Low-Rank Adaptation (LoRA)-1 fine-tuning strategy based on our proposed metric and the BaQCKV that mitigates selection bias, providing a computationally efficient alternative that maintains model generalizability. Experiments across multiple MCQ benchmarks demonstrate that our approaches reduce bias, increasing consistency in accuracy while minimizing computational costs.

Recent advances in speech‐enabled AI, including Google’s NotebookLM and OpenAI’s speech-to-speech API, are driving widespread interest in voice interfaces across sectors such as finance, health, agritech, legal services, and call‐centers in the global north and south. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa’s linguistic diversity. We present AfriSpeech‑MultiBench, the first domain‐specific evaluation suite for over 100 African English accents across 10+ countries and seven application domains: Finance, Legal, Medical, General dialogue, Call Center, Named Entities, and Hallucination Robustness. We benchmark a diverse range of open, closed, unimodal ASR and multimodal LLM-based speech recognition systems using both spontaneous and non-spontaneous speech conversations drawn from various open African accented English speech datasets. Our empirical analysis reveals systematic variation: open‐source ASR excels in spontaneous speech contexts but degrades on noisy, non‐native dialogue; multimodal LLMs are more accent‐robust yet struggle with domain‐specific named entities; proprietary models deliver high accuracy on clean speech but vary significantly by country and domain. Smaller models fine‐tuned on African English achieve competitive accuracy with lower latency, a practical advantage for deployment. By releasing this benchmark, we empower practitioners and researchers to select voice technologies suited to African use‐cases, fostering inclusive voice applications for undeserved communities.