比较中文人工智能技术中的多样性、负面性与刻板印象：对百度、文心一言与通义千问的研究
Comparing diversity, negativity, and stereotypes in Chinese-language AI technologies: an investigation of Baidu, Ernie and Qwen
AI总结：
本文通过提示百度、文心一言和通义千问对240个中国社会群体生成描述词，比较其嵌入观点的多样性、负面性与刻板印象，发现语言模型观点更多样但存在中等程度刻板印象，呼吁从全球视角促进AI公平与包容。
摘要：
大型语言模型（LLMs）和搜索引擎有可能通过放大其训练数据和算法过程中已有的偏见来延续偏见和刻板印象，从而影响公众认知和决策。尽管大多数研究集中于以西方为中心的人工智能技术，我们通过调查嵌入在中国主要搜索引擎百度和两个领先大型语言模型文心一言（Ernie）与通义千问（Qwen）中的社会偏见来研究基于中文的工具。利用一个涵盖描述中国社会的13个类别、240个社会群体的数据集，我们通过提示上述工具生成描述这些群体的候选词，收集了超过3万个嵌入在这些工具中的观点。我们发现，与搜索引擎相比，语言模型表现出更多样化的嵌入观点，尽管百度和通义千问比文心一言更频繁地生成负面内容。我们还发现语言模型中存在中等程度的刻板印象，其中许多可能助长冒犯性和贬损性观点。我们的工作强调了从全球视角促进人工智能技术公平性和包容性的重要性。
英文摘要：
Large Language Models (LLMs) and search engines have the potential to perpetuate biases and stereotypes by amplifying existing prejudices in their training data and algorithmic processes, thereby influencing public perception and decision-making. While most work has focused on Western-centric AI technologies, we study Chinese-based tools by investigating social biases embedded in the major Chinese search engine, Baidu, and two leading LLMs, Ernie and Qwen. Leveraging a dataset of 240 social groups across 13 categories describing Chinese society, we collect over 30k views encoded in the aforementioned tools by prompting them for candidate words describing such groups. We find that language models exhibit a larger variety of embedded views compared to the search engine, although Baidu and Qwen generate negative content more often than Ernie. We also find a moderate prevalence of stereotypes embedded in the language models, many of which potentially promote offensive and derogatory views. Our work highlights the importance of promoting fairness and inclusivity in AI technologies with a global perspective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
