# PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models

arXiv: https://arxiv.org/abs/2404.08699 · HTML: https://mlsanity.com/paper/2404.08699/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model](https://mlsanity.com/paper/2304.15010/) (2023)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://mlsanity.com/paper/2308.14608/) (2023)

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions](https://mlsanity.com/paper/2409.16430/) (2024)
- [Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models](https://mlsanity.com/paper/2407.06957/) (2024)
- [Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2408.04643/) (2024)
- [PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay](https://mlsanity.com/paper/2603.23841/) (2026)
- [Bioalignment: Measuring and Improving LLM Disposition Toward Biological Systems for AI Safety](https://mlsanity.com/paper/2603.09154/) (2026)
- [Adversarial Alignment: Ensuring Value Consistency in Large Language Models for Sensitive Domains](https://mlsanity.com/paper/2601.13137/) (2026)
- [Measuring Political Stance and Consistency in Large Language Models](https://mlsanity.com/paper/2601.17016/) (2026)
- [Entropy-Based Data Selection for Language Models](https://mlsanity.com/paper/2602.17465/) (2026)
- [A Multi-Dimensional Audit of Politically Aligned Large Language Models](https://mlsanity.com/paper/2604.24429/) (2026)
- [Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents](https://mlsanity.com/paper/2607.15095/) (2026)
- [Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap](https://mlsanity.com/paper/2608.27512/) (2026)

## Abstract (arXiv)

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.