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.

翻译：人工智能（AI）与自然语言处理中的性别偏见因其对社会认知与偏见的潜在影响而受到广泛关注。本研究旨在分析大型语言模型（LLMs）中的性别偏见，重点对GPT-2与GPT-3.5这两种代表性语言模型进行多重比较，以深入理解其社会影响。通过系统性文献综述，本研究梳理了AI语言模型性别偏见的现有研究成果，并识别当前知识体系中的空白。研究方法包括从GPT-2与GPT-3.5中收集并预处理数据，采用深度定量分析技术评估生成文本中的性别偏见。研究结果揭示了这些大型语言模型输出中存在的性别化词语关联、语言使用模式及偏见性叙事。讨论部分探讨了性别偏见的伦理影响及其对社会认知与边缘化社群的潜在后果。此外，论文提出了缓解LLMs性别偏见的策略，包括算法优化与数据增强技术。本研究强调跨学科合作的重要性，以及社会学研究在减轻AI模型性别偏见中的关键作用。通过解决这些问题，我们能够为构建更具包容性与无偏见的AI系统铺平道路，从而对社会产生积极影响。