标题： 播客和大型语言模型中的性别化话语中的男性默认设置 显示英文标题

标题： Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models

摘要： 男性默认值被广泛认为是一种重要的性别偏见，但它们往往未被察觉，因为研究不足。 男性默认值包括三个关键部分：(i) 文化背景，(ii) 男性特征或行为，以及 (iii) 对这些男性特征或行为的奖励或仅仅是接受。 在本工作中，我们研究基于话语的男性默认值，并提出了一个两部分框架：(i) 通过我们的性别话语相关框架（GDCF）大规模发现和分析口语内容中的性别化话语词；以及 (ii) 通过我们的话语词嵌入关联测试（D-WEAT）测量这些性别化话语词与大型语言模型（LLMs）相关的性别偏见。 我们专注于播客，这是一种流行且不断增长的社交媒体形式，分析了15,117个播客节目。 我们分析了性别与话语词之间的相关性——通过LDA和BERTopic发现的——以自动生成性别化话语词列表。 然后，我们研究了这些性别化话语词在特定领域背景中的普遍性，并发现基于性别化话语的男性默认值存在于商业、技术和政治以及视频游戏领域。 接下来，我们研究了来自OpenAI的一个最先进的LLM嵌入模型中这些性别化话语词的表示，并发现男性话语词比女性话语词具有更稳定和稳健的表示，这可能导致在下游任务中对男性有更好的系统性能。 因此，男性因他们的话语模式而得到更好的系统性能，这是由一个最先进的语言模型带来的——这种嵌入差异是一种表征伤害和男性默认值。 显示英文摘要

摘要： Masculine defaults are widely recognized as a significant type of gender bias, but they are often unseen as they are under-researched. Masculine defaults involve three key parts: (i) the cultural context, (ii) the masculine characteristics or behaviors, and (iii) the reward for, or simply acceptance of, those masculine characteristics or behaviors. In this work, we study discourse-based masculine defaults, and propose a twofold framework for (i) the large-scale discovery and analysis of gendered discourse words in spoken content via our Gendered Discourse Correlation Framework (GDCF); and (ii) the measurement of the gender bias associated with these gendered discourse words in LLMs via our Discourse Word-Embedding Association Test (D-WEAT). We focus our study on podcasts, a popular and growing form of social media, analyzing 15,117 podcast episodes. We analyze correlations between gender and discourse words -- discovered via LDA and BERTopic -- to automatically form gendered discourse word lists. We then study the prevalence of these gendered discourse words in domain-specific contexts, and find that gendered discourse-based masculine defaults exist in the domains of business, technology/politics, and video games. Next, we study the representation of these gendered discourse words from a state-of-the-art LLM embedding model from OpenAI, and find that the masculine discourse words have a more stable and robust representation than the feminine discourse words, which may result in better system performance on downstream tasks for men. Hence, men are rewarded for their discourse patterns with better system performance by one of the state-of-the-art language models -- and this embedding disparity is a representational harm and a masculine default.