A Systematic Analysis of Biases in Large Language Models

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A systematic analysis investigated biases in four widely adopted large language models: Qwen2.5-7B-Instruct, DeepSeek-V3-0324, Gemini-2.5-flash, and GPT-4o-mini. The study employed five distinct experiments to probe biases across politics, ideology, geopolitical alliances, language, and gender. Findings indicate that while LLMs generally aim for neutrality, they exhibit specific inclinations. For instance, Gemini showed a right-leaning political tendency and aligned with right-wing ideologies, while GPT leaned slightly left. In geopolitical alliance, Gemini best simulated UN voting patterns, aligning with Latin American and African delegates, and notably disagreeing with the US. All models aligned more with women's values, with GPT showing a 36.77% difference. Language bias revealed Southern African languages clustering near English for Qwen, DeepSeek, and Gemini, but GPT showed the most diverse multilingual thinking. The research concludes that LLMs perpetuate biases from training data, despite alignment efforts.

Key takeaway

For machine learning engineers deploying LLMs in sensitive applications, you must rigorously evaluate your chosen model for inherent biases across political, ideological, geopolitical, language, and gender dimensions. Do not assume neutrality; instead, conduct targeted bias assessments using methods like those presented, especially for models like Gemini (right-leaning) or GPT (women's values alignment). This proactive testing is crucial to prevent perpetuating stereotypes and ensuring responsible AI deployment, potentially requiring pluralistic LLM approaches or alternative intelligence alignment theories.

Key insights

LLMs, despite alignment efforts, inherit and perpetuate diverse biases from human-generated training data.

Principles

• LLM alignment efforts do not guarantee bias-free outputs.
• Biases manifest differently across models and domains.
• Human-centric alignment can transfer human prejudices.

Method

The study systematically probed LLM biases using five distinct experimental setups: news summarization for political bias, stance classification for ideological bias, UN voting simulation for alliance bias, multilingual story completion for language bias, and World Values Survey responses for gender bias.

In practice

• Evaluate LLMs for specific biases relevant to your application.
• Consider model-specific biases (e.g., Gemini's right-leaning tendencies).
• Be aware of potential "contracting values" in some LLMs.

Topics

• Large Language Models
• AI Bias
• Model Evaluation
• Political Bias
• Gender Bias
• Geopolitical Alignment

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.