Decoding the Mind of Large Language Models: A Quantitative Evaluation of Ideology and Biases

2025-05-18

AI Summary

This work addresses the lack of interpretability regarding ideological and ethical biases in large language models (LLMs). We propose the first quantitative evaluation paradigm grounded in value trade-offs. Our framework comprises 436 bilingual, binary-choice questions—each lacking a single correct answer—to systematically assess ideological leanings, ethical reasoning, and linguistic dependency across models. Applying behavioral analysis, statistical significance testing, and bias intensity scoring, we uncover substantial and inconsistent stance preferences (e.g., “opinion accommodation”) in ChatGPT and Gemini, alongside multiple outputs violating fairness and ethical principles. The framework ensures reproducibility, cross-model comparability, and cross-lingual validity. It provides a transparent, scalable, and auditable quantitative tool for evaluating social alignment in AI systems.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The widespread integration of Large Language Models (LLMs) across various sectors has highlighted the need for empirical research to understand their biases, thought patterns, and societal implications to ensure ethical and effective use. In this study, we propose a novel framework for evaluating LLMs, focusing on uncovering their ideological biases through a quantitative analysis of 436 binary-choice questions, many of which have no definitive answer. By applying our framework to ChatGPT and Gemini, findings revealed that while LLMs generally maintain consistent opinions on many topics, their ideologies differ across models and languages. Notably, ChatGPT exhibits a tendency to change their opinion to match the questioner's opinion. Both models also exhibited problematic biases, unethical or unfair claims, which might have negative societal impacts. These results underscore the importance of addressing both ideological and ethical considerations when evaluating LLMs. The proposed framework offers a flexible, quantitative method for assessing LLM behavior, providing valuable insights for the development of more socially aligned AI systems.

Problem

• Quantifying ideological biases in Large Language Models (LLMs)
• Assessing ethical and societal impacts of LLM biases
• Developing a framework to evaluate LLM behavior consistency

Innovation

• Quantitative framework evaluates LLM ideological biases
• Analyzes 436 binary-choice questions for biases
• Flexible method assesses ChatGPT and Gemini