大型语言模型中性别刻板印象表征的实证调查：意大利语案例
An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case
AI总结：
本文通过意大利语职业层级提示实验，测试ChatGPT与Gemini的性别职业偏见，发现二者均显著将女性代词关联于低层职位。
摘要：
大型语言模型（LLM）在众多领域的日益广泛使用，引发了人们对其多么容易延续刻板印象并促成生成有偏见内容的担忧。本研究聚焦性别与职业偏见，考察LLM以何种方式塑造对未指定性别的提示的回答，从而导致有偏见的输出。该分析采用结构化实验方法，给出涉及三种不同职业组合的不同提示，这些职业组合还具有层级关系特征。本研究使用意大利语这一具有广泛语法性别差异的语言，以揭示当前LLM在非英语语言中生成客观文本能力方面的潜在局限。研究考察了两款流行的基于LLM的聊天机器人，即OpenAI ChatGPT（gpt-4o-mini）和Google Gemini（gemini-1.5-flash）。通过API，我们收集了3600条回复。结果凸显了LLM生成的内容如何会延续刻板印象。例如，Gemini将100%（ChatGPT为97%）的“她”代词与“助理”而非“经理”关联起来。AI生成文本中偏见的存在可能在工作场所或人员选拔等许多领域产生重大影响，引发对其使用的伦理担忧。理解这些风险对于制定缓解策略、确保基于AI的系统不会加剧社会不平等，而是促进更公平的结果至关重要。未来研究方向包括将研究扩展到更多聊天机器人或语言，改进提示工程方法，或进一步利用更大的实验基础。
英文摘要：
The increasing use of Large Language Models (LLMs) in a large variety of domains has sparked worries about how easily they can perpetuate stereotypes and contribute to the generation of biased content. With a focus on gender and professional bias, this work examines in which manner LLMs shape responses to ungendered prompts, contributing to biased outputs. This analysis uses a structured experimental method, giving different prompts involving three different professional job combinations, which are also characterized by a hierarchical relationship. This study uses Italian, a language with extensive grammatical gender differences, to highlight potential limitations in current LLMs' ability to generate objective text in non-English languages. Two popular LLM-based chatbots are examined, namely OpenAI ChatGPT (gpt-4o-mini) and Google Gemini (gemini-1.5-flash). Through APIs, we collected a range of 3600 responses. The results highlight how content generated by LLMs can perpetuate stereotypes. For example, Gemini associated 100% (ChatGPT 97%) of 'she' pronouns to the 'assistant' rather than the 'manager'. The presence of bias in AI-generated text can have significant implications in many fields, such as in the workplaces or in job selections, raising ethical concerns about its use. Understanding these risks is pivotal to developing mitigation strategies and assuring that AI-based systems do not increase social inequalities, but rather contribute to more equitable outcomes. Future research directions include expanding the study to additional chatbots or languages, refining prompt engineering methods or further exploiting a larger experimental base.
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。
