缩小视野：量化生成性单一文化中的主题显著性变化
Narrowing the Horizon: Quantifying Topic Saliency Shifts in Generative Monoculture
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中文总结 AI 辅助
本文提出量化生成性单一文化中主题显著性变化的方法，通过案例研究揭示同质化影响，并证明专门化模型可保留观点多样性。
中文摘要 AI 辅助
随着大型语言模型（LLMs）成为我们获取和分享信息的核心工具，它们在塑造全球知识方面发挥着越来越强大的作用。然而，随着这些模型的发展，其输出可能趋于收敛为一种“生成性单一文化”，即它们所代表的观点多样性随时间推移而缩小。模型层面的研究往往无法精确指出在这一过程中哪些特定主题或观点被边缘化或放大。在本文中，我们提出了一种方法，用于衡量跨模型家族的主题显著性变化，追踪在后训练阶段哪些内容获得或失去突出地位。将这种方法应用于气候变化话语的案例研究，我们展示了同质化如何影响不同模型对多样化解决方案的表征。我们还测试了应对这一趋势的干预措施，表明专门化模型有助于保留更广泛的观点。这强调了监测主题显著性以诊断单一文化风险并确保AI系统反映思想多元性的重要性。数据和代码可在此处获取。
英文摘要
As Large Language Models (LLMs) become central to how we access and share information, they play an increasingly powerful role in shaping global knowledge. However, as these models evolve, their outputs risk converging into a \textit{generative monoculture}, where the diversity of perspectives they represent narrows over time. Studies at the model level often fail to pinpoint which specific topics or viewpoints are being marginalised or amplified in this process. In this paper, we introduce a method to measure shifts in topic saliency across model families, tracking what gains or loses prominence during post-training. Applying this approach to a case study of climate change discourse, we demonstrate how homogenisation affects the representation of diverse solutions across different models. We also test interventions to counter this trend, showing that specialised models can help preserve a broader range of perspectives. This underscores the importance of monitoring topic saliency to diagnose the risks of monoculture and to ensure AI systems reflect a pluralism of ideas. Data and Code are accessible \href{https://github.com/oriane/topic_saliency_shift}{here}.
发表机构
- King’s College London（伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。
