LLMs' Leaning in European Elections

The analysis of LLM biases is an active research field. As the application of LLMs in decision-making activities is increasing, their study is critical to better understand their implications on decisional processes. The coherence and the structural preferences that these models are acquiring over several topics could challenge their applications in several fields [4]. The origin of these biases is complicated to study and could come from different steps in LLM training. For example, these biases could be acquired during the pre-training phase, supervised fine-tuning phase, or even during the final alignment phase. This article focuses on understanding the extent of the political biases of LLM through two experiments. The first experiment has the objective of showing the left leaning of multiple LLMs in the context of several virtual European elections, section 4.1. The second experiment shows that LLMs consider "stupidity" and "ignorance" as human characteristics that make voting for the right wing more probable, section 4.2. As different models could exhibit different leans, we tested four of the most used LLMs in both our experiments, Table 1.