生成式AI写作辅助中的议题偏见：瑞典2026年选举中的政治议题与LLM
Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election
- Chalmers University of Technology（查尔姆斯理工大学）
- University of Gothenburg（哥德堡大学）
- Lund University（隆德大学）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过大规模提示组合测试六个LLM在瑞典2026年大选前的立场，发现模型间存在议题相关偏见，且社会民主党与所有模型立场最接近，但无模型表现出明确的政党偏好。
AI中文摘要：
生成式AI写作助手及其驱动的大语言模型（LLM）正日益成为选民在选举前获取信息的方式之一。随着越来越多的证据表明这些工具会影响用户的观点，理解这些工具的立场和态度变得愈发重要。为了更好地理解这些观点，我们在2026年瑞典议会选举前，考察了六个LLM在各种瑞典语写作任务中提供的立场。我们将107项政策主张与77种写作模板以及中性、正面和负面的提示框架进行交叉组合，为每个模型生成了24,717个提示，共获得148,302个响应。为了研究这些响应，我们分析了模型的默认立场倾向，比较了它们对相似议题的回应方式，并将其回应与瑞典八个议会政党在同一议题上的立场进行了比较。我们发现，Claude、DeepSeek、Gemini和Mistral具有相似的立场特征；ChatGPT更常提供中性或模棱两可的文本；而Grok在移民、犯罪和性别等议题上差异最大。在与政党进行比较时，我们发现社会民主党与所有六个模型的立场最为接近。尽管如此，在进行多重比较校正后，各模型内部政党距离的差异均不显著。总体而言，我们发现没有模型表现出明确的偏好，也没有对某个政党的明确倾向，但这取决于用户所询问的具体议题或任务。
英文摘要：
Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before elections. With growing evidence that they influence users' opinions, it is increasingly important to understand the views and positions of these tools. To better understand these views, we examine the stances supplied by six LLMs on a variety of Swedish-language writing tasks ahead of the 2026 Swedish parliamentary election. We cross 107 policy propositions with 77 writing templates and neutral, positive, and negative prompt framings, producing 24,717 prompts per model and 148,302 responses. To study these, we look at the models' default stance tendencies, compare how they respond to similar issues, and compare their responses with those of each of Sweden's eight parliamentary parties on the same issue. We find that Claude, DeepSeek, Gemini, and Mistral have similar profiles; ChatGPT more often supplies neutral or ambivalent text; and Grok differs most on topics such as migration, crime, and gender. When comparing the political parties, we find that the Social Democrats are closest to all six models. Still, after correcting for multiple comparisons, none of the within-model differences in party distances remains significant. Overall, we find that no model has a clear preference, nor a clear preference for a party, but that this depends on the specific issue or task the user asks about.
