Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains but have introduced substantial biases that can compromise their effectiveness and fairness in real-world applications [1] [2]. This experimental study investigates the prevalence and manifestation of bias in contemporary LLMs, evaluates existing detection methodologies, and assesses the effectiveness of mitigation strategies. We conducted a comprehensive analysis using multiple bias ev...
