Mohamed Mostagir | Searcharxiv

arXiv2026

A Multi-Dimensional Audit of Politically Aligned Large Language Models

As the application of Large Language Models (LLMs) spreads across various industries, there are increasing concerns about the potential for their misuse, especially in sensitive areas such as political discourse. Deliberately aligning LLMs with specific political ideologies, through prompt engineering or fine-tuning techniques, can be advantageous in use cases such as political campaigns, but requires careful consideration due to heightened risks of performance degradation, misinformation, or increased biased behavior. In this work, we propose a multi-dimensional framework inspired by Habermas' Theory of Communicative Action to audit politically aligned language models across four dimensions: effectiveness, fairness, truthfulness, and persuasiveness using automated, quantitative metrics. Applying this to nine popular LLMs aligned via fine-tuning or role-playing revealed consistent trade-offs: while larger models tend to be more effective at role-playing political ideologies and truthful in their responses, they were also less fair, exhibiting higher levels of bias in the form of angry and toxic language towards people of different ideologies. Fine-tuned models exhibited lower bias and more effective alignment than the corresponding role-playing models, but also saw a decline in performance reasoning tasks and an increase in hallucinations. Overall, all of the models tested exhibited some deficiency in at least one of the four metrics, highlighting the need for more balanced and robust alignment strategies. Ultimately, this work aims to ensure politically-aligned LLMs generate legitimate, harmless arguments, offering a framework to evaluate the responsible political alignment of these models.

cs.CL↗

arXiv2024

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

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.

cs.CL↗