To address this issue, we formulate translated non-English, geographic, and
socioeconomic integrated prompts and evaluate their impact on VL model
performance for data from different countries and income groups. Our findings
show that geographic and socioeconomic integrated prompts improve VL
performance on lower-income data and favor the retrieval of topic appearances
commonly found in data from low-income households. From our analyses, we
identify and highlight contexts where these strategies yield the most
improvements. Our model analysis code is publicly available at
this https URL .

通过使用翻译的非英语、地理和社会经济一体化提示，我们评估了它们对来自不同国家和收入群体的可见光模型性能的影响。我们的发现表明，地理和社会经济一体化提示可以提高低收入数据上的可见光模型性能，并有利于检索低收入家庭数据中常见的主题。从我们的分析中，我们确定和突出了这些策略产生最大改进的情境。

提升低收入数据：视觉语言模型中实现社会经济视角转变的策略

Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective  Shifts in Vision-Language Models

Understanding movement described in text documents is important since text
descriptions of movement contain a wealth of geographic and contextual
information about the movement of people, wildlife, goods, and much more. Our
research makes several contributions to improve our understanding of movement
descriptions in text. First, we show how interpreting geographic movement
described in text is challenging because of general spatial terms, linguistic
constructions that make the thing(s) moving unclear, and many types of temporal
references and groupings, among others. Next, as a step to overcome these
challenges, we report on an experiment with human subjects through which we
identify multiple important characteristics of movement descriptions (found in
text) that humans use to differentiate one movement description from another.
Based on our empirical results, we provide recommendations for computational
analysis using movement described in text documents. Our findings contribute
towards an improved understanding of the important characteristics of the
underused information about geographic movement that is in the form of text
descriptions.

研究文本中描述的运动是有挑战性的，由于空间 term，语言构造以及时间参照等多因素影响。研究通过实验，发现人们用来区分不同运动描述的特征，为基于文本的运动计算分析提出了建议。

文本描述的地理移动划分

Differentiating Geographic Movement Described in Text Documents

Computer-mediated communication is driving fundamental changes in the nature
of written language. We investigate these changes by statistical analysis of a
dataset comprising 107 million Twitter messages (authored by 2.7 million unique
user accounts). Using a latent vector autoregressive model to aggregate across
thousands of words, we identify high-level patterns in diffusion of linguistic
change over the United States. Our model is robust to unpredictable changes in
Twitter's sampling rate, and provides a probabilistic characterization of the
relationship of macro-scale linguistic influence to a set of demographic and
geographic predictors. The results of this analysis offer support for prior
arguments that focus on geographical proximity and population size. However,
demographic similarity -- especially with regard to race -- plays an even more
central role, as cities with similar racial demographics are far more likely to
share linguistic influence. Rather than moving towards a single unified
"netspeak" dialect, language evolution in computer-mediated communication
reproduces existing fault lines in spoken American English.

通过分析 107 百万条推特信息，我们发现计算机媒介通信正在驱动书面语言的根本变革，语言演化并不使得美国英语变得一致，而是重现了口语中现有的毛病。