  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
LLMs. In this context, we introduce PoliTune, a fine-tuning methodology to
explore the systematic aspects of aligning LLMs with specific ideologies,
mindful of the biases that arise from their extensive training on diverse
datasets. Distinct from earlier efforts that either focus on smaller models or
entail resource-intensive pre-training, PoliTune employs Parameter-Efficient
Fine-Tuning (PEFT) techniques, which allow for the alignment of LLMs with
targeted ideologies by modifying a small subset of parameters. We introduce a
systematic method for using the open-source LLM Llama3-70B for dataset
selection, annotation, and synthesizing a preferences dataset for Direct
Preference Optimization (DPO) to align the model with a given political
ideology. We assess the effectiveness of PoliTune through both quantitative and
qualitative evaluations of aligning open-source LLMs (Llama3-8B and Mistral-7B)
to different ideologies. 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.
