Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameters

• URL: http://arxiv.org/abs/2505.04393v1
• Date: Wed, 07 May 2025 13:18:41 GMT
• Title: Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameters
• Authors: David Exler, Mark Schutera, Markus Reischl, Luca Rettenberger,
• Abstract summary: Large language models (LLMs) are predominantly used by many as a primary source of information for various topics.<n>LLMs frequently make factual errors, fabricate data (hallucinations), or present biases, exposing users to misinformation and influencing opinions.<n>We quantify the political bias of popular LLMs in the context of the recent vote of the German Bundestag using the score produced by the Wahl-O-Mat.
• Score: 0.571853823214391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing prevalence of artificial intelligence, careful evaluation of inherent biases needs to be conducted to form the basis for alleviating the effects these predispositions can have on users. Large language models (LLMs) are predominantly used by many as a primary source of information for various topics. LLMs frequently make factual errors, fabricate data (hallucinations), or present biases, exposing users to misinformation and influencing opinions. Educating users on their risks is key to responsible use, as bias, unlike hallucinations, cannot be caught through data verification. We quantify the political bias of popular LLMs in the context of the recent vote of the German Bundestag using the score produced by the Wahl-O-Mat. This metric measures the alignment between an individual's political views and the positions of German political parties. We compare the models' alignment scores to identify factors influencing their political preferences. Doing so, we discover a bias toward left-leaning parties, most dominant in larger LLMs. Also, we find that the language we use to communicate with the models affects their political views. Additionally, we analyze the influence of a model's origin and release date and compare the results to the outcome of the recent vote of the Bundestag. Our results imply that LLMs are prone to exhibiting political bias. Large corporations with the necessary means to develop LLMs, thus, knowingly or unknowingly, have a responsibility to contain these biases, as they can influence each voter's decision-making process and inform public opinion in general and at scale.

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