arXiv:2606.12922v1 Announce Type: new Abstract: Political bias in large language models (LLMs) is increasingly significant, but difficult to measure reproducibly across political and linguistic contexts. We introduce Polar, a 4,026-instance multiple-choice benchmark that measures political bias through option-level likelihoods rather than prompt-based generation. Polar covers two ideological axes and eight issue categories derived from the Manifesto Project, and evaluates models in parallel across U.S. and South Korean political contexts. Across 38 LLMs, measured bias varies systematically wit
The proliferation of LLMs into critical applications and information dissemination highlights an urgent need for robust and reproducible bias measurement as their influence grows.
This benchmark provides a standardized, multi-contextual method to quantify political bias in LLMs, which is crucial for ethical AI development, regulation, and public trust.
The ability to systematically compare LLM biases across different political contexts and issue categories will allow for more targeted mitigation strategies and expose hidden ideological leanings.
- · AI ethics researchers
- · Regulatory bodies
- · Independent LLM developers
- · LLMs with unmitigated biases
- · Platforms relying on biased LLMs
- · Developers ignoring bias detection
Specific LLMs will be identified as having measurable political biases in certain contexts.
This will drive developers to implement more sophisticated fine-tuning and alignment techniques to reduce or control political biases.
The benchmark could become a de facto standard for 'bias certification' of LLMs, influencing market adoption and regulatory compliance.
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Read at arXiv cs.CL