Analyzing the Impact of Data Selection and Fine-Tuning on Economic and
  Political Biases in LLMs
Ahmed Agiza Mohamed Mostagir Sherief Reda
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.