Echoing the history of search engines and social media content rankings, the
advent of large language models (LLMs) has led to a push for increased
personalization of model outputs to individual users. In the past, personalized
recommendations and ranking systems have been linked to the development of
filter bubbles (serving content that may confirm a user's existing biases) and
affective polarization (strong negative sentiment towards those with differing
views). In this work, we explore how prompting a leading large language model,
ChatGPT-3.5, with a user's political affiliation prior to asking factual
questions about public figures and organizations leads to differing results. We
observe that left-leaning users tend to receive more positive statements about
left-leaning political figures and media outlets, while right-leaning users see
more positive statements about right-leaning entities. This pattern holds
across presidential candidates, members of the U.S. Senate, and media
organizations with ratings from AllSides. When qualitatively evaluating some of
these outputs, there is evidence that particular facts are included or excluded
based on the user's political affiliation. These results illustrate that
personalizing LLMs based on user demographics carry the same risks of affective
polarization and filter bubbles that have been seen in other personalized
internet technologies. This ``failure mode" should be monitored closely as
there are more attempts to monetize and personalize these models.

对个人化的大型语言模型输出进行用户政治倾向性的引导，发现左倾用户更容易接收到对左派政界人物和媒体机构的正面评价，而右倾用户则更容易接收到对右派实体的正面评价。这种个人化模式会带来情感极化和过滤泡沫的风险。

用户个性化大型语言模型输出中的筛选泡和情感极化

Filter bubbles and affective polarization in user-personalized large  language model outputs

Word embeddings provide an unsupervised way to understand differences in word
usage between discursive communities. A number of recent papers have focused on
identifying words that are used differently by two or more communities. But
word embeddings are complex, high-dimensional spaces and a focus on identifying
differences only captures a fraction of their richness. Here, we take a step
towards leveraging the richness of the full embedding space, by using word
embeddings to map out how words are used differently. Specifically, we describe
the construction of dialectograms, an unsupervised way to visually explore the
characteristic ways in which each community use a focal word. Based on these
dialectograms, we provide a new measure of the degree to which words are used
differently that overcomes the tendency for existing measures to pick out low
frequent or polysemous words. We apply our methods to explore the discourses of
two US political subreddits and show how our methods identify stark affective
polarisation of politicians and political entities, differences in the
assessment of proper political action as well as disagreement about whether
certain issues require political intervention at all.

利用词向量建立方言图，提供新的度量词语差异的方法，探究两个美国政治子版的话语，并显示出政治家和政治实体情绪上的极端相反、对适当的政治行为评估的差异以及关于是否需要政治干预某些问题的分歧。