In an era where language models are increasingly integrated into
decision-making and communication, understanding the biases within Large
Language Models (LLMs) becomes imperative, especially when these models are
applied in the economic and political domains. This work investigates the
impact of fine-tuning and data selection on economic and political biases in
LLM. We explore the methodological aspects of biasing LLMs towards specific
ideologies, mindful of the biases that arise from their extensive training on
diverse datasets. Our approach, distinct from earlier efforts that either focus
on smaller models or entail resource-intensive pre-training, employs
Parameter-Efficient Fine-Tuning (PEFT) techniques. These techniques allow for
the alignment of LLMs with targeted ideologies by modifying a small subset of
parameters. We introduce a systematic method for dataset selection, annotation,
and instruction tuning, and we assess its effectiveness through both
quantitative and qualitative evaluations. Our work analyzes the potential of
embedding specific biases into LLMs and contributes to the dialogue on the
ethical application of AI, highlighting the importance of deploying AI in a
manner that aligns with societal values.

本研究探究了大型语言模型中的经济和政治偏见以及调整细节对其产生的影响，通过使用参数效率微调技术，我们介绍了一种系统的数据集选择、注释和指令调整方法，并通过定量和定性评估评估其有效性，旨在讨论 AI 的道德应用，强调将 AI 部署在符合社会价值观的方式上的重要性。

分析对数据选择和微调对 LLMs 中的经济和政治偏见的影响

Analyzing the Impact of Data Selection and Fine-Tuning on Economic and  Political Biases in LLMs

This paper addresses polarization quantification, particularly as it pertains
to the nomination of Brett Kavanaugh to the US Supreme Court and his subsequent
confirmation with the narrowest margin since 1881. Republican (GOP) and
Democratic (DNC) senators voted overwhelmingly along party lines. In this
paper, we examine political polarization concerning the nomination among
Twitter users. To do so, we accurately identify the stance of more than 128
thousand Twitter users towards Kavanaugh's nomination using both
semi-supervised and supervised classification. Next, we quantify the
polarization between the different groups in terms of who they retweet and
which hashtags they use. We modify existing polarization quantification
measures to make them more efficient and more effective. We also characterize
the polarization between users who supported and opposed the nomination.

本文使用半监督和监督分类技术，分析 Twitter 用户对布雷特・卡瓦诺（Brett Kavanaugh）的提名立场，并通过修改现有的极化量化措施，表征了支持和反对提名的用户之间的极化。