Large language models such as ChatGPT often exhibit striking political biases. If users query them about political information, they might take a normative stance and reinforce such biases. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Such aligned models are able to generate more accurate political viewpoints from Swiss parties compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews from multiple viewpoints using such models.

翻译：诸如ChatGPT之类的大型语言模型常表现出显著的政治偏见。当用户查询政治信息时，这些模型可能采取规范性立场并强化此类偏见。为克服此问题，我们利用瑞士国民议会候选人撰写的10万条评论，将大型语言模型与多元政治观点进行对齐。相较于ChatGPT等商业模型，经对齐的模型能生成更准确的瑞士政党政治观点。我们还提出一种利用此类模型从多视角生成平衡性综述的方法。