Language models now constitute essential tools for improving efficiency for
many professional tasks such as writing, coding, or learning. For this reason,
it is imperative to identify inherent biases. In the field of Natural Language
Processing, five sources of bias are well-identified: data, annotation,
representation, models, and research design. This study focuses on biases
related to geographical knowledge. We explore the connection between geography
and language models by highlighting their tendency to misrepresent spatial
information, thus leading to distortions in the representation of geographical
distances. This study introduces four indicators to assess these distortions,
by comparing geographical and semantic distances. Experiments are conducted
from these four indicators with ten widely used language models. Results
underscore the critical necessity of inspecting and rectifying spatial biases
in language models to ensure accurate and equitable representations.

研究语言模型中的地理偏见，通过比较地理和语义距离，提出四个指标来评估这些偏见，结果强调了检查和纠正语言模型的空间偏见对于确保准确和公平的表达是至关重要的。

评估语言模型中的地理扭曲：迈向公平表达的关键一步

Evaluation of Geographical Distortions in Language Models: A Crucial  Step Towards Equitable Representations

Few-shot text classification has attracted great interest in both academia
and industry due to the lack of labeled data in many fields. Different from
general text classification (e.g., topic classification), few-shot sentiment
classification is more challenging because the semantic distances among the
classes are more subtle. For instance, the semantic distances between the
sentiment labels in a positive or negative polarity (e.g., ``love" and ``joy",
``remorse" and ``sadness") are close, while the distances are large for the
sentiment labels in two opposite polarities (e.g., ``love" and ``sadness"). To
address this problem, we propose a Soft Contrastive learning-based Prompt
(\texttt{SCP}) model for few-shot sentiment analysis. First, we design a
sentiment-aware chain of thought prompt module to guide the model to predict
the sentiment from coarse grain to fine grain via a series of intermediate
reasoning steps. Then, we propose a soft contrastive learning algorithm to take
the correlation of the labels into account. A series of experiments on several
sentiment analysis datasets show the great advantages of \texttt{SCP} by
comparing it with SOTA baselines (e.g., ChatGPT).

我们提出了基于软对比学习的 Prompt (SCP) 模型对少样本情感分析进行研究，通过舆论模块引导模型进行从粗粒度到细粒度的情感预测，并考虑标签之间的相关性，与其他基准模型进行对比实验证明了 SCP 的巨大优势。