Saturday 27 September 2025
A recent study has shed light on the biases present in large language models (LLMs) designed for political contexts. Researchers introduced a novel benchmark, EuroParlVote, which links European Parliament debate speeches to roll-call vote outcomes and includes detailed demographic information about each Member of the European Parliament (MEP). By evaluating state-of-the-art LLMs on two tasks – gender classification and vote prediction – the study reveals consistent patterns of bias.
The researchers found that LLMs frequently misclassify female MEPs as male, indicating a lack of understanding about the speakers’ identities. Moreover, when simulating votes for female speakers, these models demonstrated reduced accuracy compared to their performance with male speakers. Politically, the models tended to favor centrist groups while underperforming on both far-left and far-right ones.
The study’s findings are concerning, as they suggest that LLMs may perpetuate biases present in political discourse. These biases can have real-world consequences, influencing how people perceive and engage with political information. In the context of the European Parliament, where gender equality is a key issue, these biases may exacerbate existing disparities.
To mitigate these biases, the researchers propose using more diverse training datasets that include a broader range of voices and perspectives. They also suggest incorporating techniques designed to reduce bias in LLMs, such as adversarial training or debiasing algorithms. Additionally, the study highlights the need for transparency and accountability in the development and evaluation of LLMs used in political contexts.
The EuroParlVote dataset provides a valuable resource for researchers seeking to explore these issues further. By releasing this data, the authors aim to support future research on fairness and accountability in NLP within political contexts. This effort may ultimately lead to more accurate and inclusive language models that better serve citizens and policymakers alike.
In the study’s evaluation of LLMs, several models demonstrated notable biases. For instance, one model incorrectly predicted a vote based solely on the speaker’s gender, while another model consistently favored centrist groups over others. These results underscore the need for careful consideration in the development and deployment of LLMs used in political contexts.
The researchers’ findings have important implications for the use of LLMs in various domains. As these models become increasingly prevalent, it is essential to recognize the potential biases they may introduce and take steps to mitigate them. By addressing these issues head-on, developers can create more accurate and trustworthy language models that serve the public interest.
Cite this article: “Biases in Large Language Models Exposed: A Study on Political Contexts”, The Science Archive, 2025.
Large Language Models, Bias, Political Contexts, European Parliament, Gender Classification, Vote Prediction, Demographic Information, Meps, Fairness, Accountability
