  Gender bias in artificial intelligence (AI) and natural language processing
has garnered significant attention due to its potential impact on societal
perceptions and biases. This research paper aims to analyze gender bias in
Large Language Models (LLMs) with a focus on multiple comparisons between GPT-2
and GPT-3.5, some prominent language models, to better understand its
implications. Through a comprehensive literature review, the study examines
existing research on gender bias in AI language models and identifies gaps in
the current knowledge. The methodology involves collecting and preprocessing
data from GPT-2 and GPT-3.5, and employing in-depth quantitative analysis
techniques to evaluate gender bias in the generated text. The findings shed
light on gendered word associations, language usage, and biased narratives
present in the outputs of these Large Language Models. The discussion explores
the ethical implications of gender bias and its potential consequences on
social perceptions and marginalized communities. Additionally, the paper
presents strategies for reducing gender bias in LLMs, including algorithmic
approaches and data augmentation techniques. The research highlights the
importance of interdisciplinary collaborations and the role of sociological
studies in mitigating gender bias in AI models. By addressing these issues, we
can pave the way for more inclusive and unbiased AI systems that have a
positive impact on society.
