A Multifaceted Analysis of Social Biases in Large Language Models

This study analyzes biases in four widely used large language models (LLMs), focusing on their political, ideological, linguistic, and gender-related tendencies. Through experiments such as news summarization and stance classification, the research reveals that while these models are designed for neutrality, they still exhibit various biases. Understanding these biases is crucial for practitioners, as it informs the development of fairer and more responsible AI systems.